Running 'PairedPSCBS,boot.R' [5s]
  Running 'findLargeGaps.R' [1s]
  Running 'randomSeed.R' [1s]
  Running 'segmentByCBS,calls.R' [0s]
  Running 'segmentByCBS,futures.R' [179s]
  Running 'segmentByCBS,median.R' [3s]
  Running 'segmentByCBS,prune.R' [174s]
  Running 'segmentByCBS,report.R' [167s]
  Running 'segmentByCBS,shiftTCN.R' [172s]
  Running 'segmentByCBS,weights.R' [166s]
  Running 'segmentByCBS.R' [141s]
  Running 'segmentByNonPairedPSCBS,medianDH.R' [3s]
  Running 'segmentByPairedPSCBS,DH.R' [159s]
  Running 'segmentByPairedPSCBS,calls.R' [179s]
  Running 'segmentByPairedPSCBS,futures.R' [172s]
  Running 'segmentByPairedPSCBS,noNormalBAFs.R' [168s]
  Running 'segmentByPairedPSCBS,report.R' [162s]
  Running 'segmentByPairedPSCBS,seqOfSegmentsByDP.R' [7s]
  Running 'segmentByPairedPSCBS.R' [9s]
Running the tests in 'tests/segmentByCBS,futures.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Simulating copy-number data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > set.seed(0xBEEF)
  > 
  > # Number of loci
  > J <- 1000
  > 
  > mu <- double(J)
  > mu[200:300] <- mu[200:300] + 1
  > mu[350:400] <- NA # centromere
  > mu[650:800] <- mu[650:800] - 1
  > eps <- rnorm(J, sd=1/2)
  > y <- mu + eps
  > x <- sort(runif(length(y), max=length(y))) * 1e5
  > w <- runif(J)
  > w[650:800] <- 0.001
  > 
  > ## Create multiple chromosomes
  > data <- knownSegments <- list()
  > for (cc in 1:3) {
  +   data[[cc]] <- data.frame(chromosome=cc, y=y, x=x)
  +   knownSegments[[cc]] <- data.frame(
  +     chromosome=c(   cc,  cc,  cc),
  +     start     =x[c(  1, 350, 401)],
  +     end       =x[c(349, 400,   J)]
  +   )
  + }
  > data <- Reduce(rbind, data)
  > str(data)
  'data.frame':	3000 obs. of  3 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ y         : num  0.295 0.115 -0.194 -0.392 -0.518 ...
   $ x         : num  55168 593204 605649 630624 746896 ...
  > knownSegments <- Reduce(rbind, knownSegments)
  > str(knownSegments)
  'data.frame':	9 obs. of  3 variables:
   $ chromosome: int  1 1 1 2 2 2 3 3 3
   $ start     : num  55168 34194740 41080533 55168 34194740 ...
   $ end       : num  34142178 41044125 99910827 34142178 41044125 ...
  > 
  > message("*** segmentByCBS() via futures ...")
  *** segmentByCBS() via futures ...
  > 
  > 
  > message("*** segmentByCBS() via futures with 'future' attached ...")
  *** segmentByCBS() via futures with 'future' attached ...
  > library("future")
  > oplan <- plan()
  > 
  > strategies <- c("sequential", "multisession")
  > 
  > ## Test 'future.batchtools' futures?
  > pkg <- "future.batchtools"
  > if (require(pkg, character.only=TRUE)) {
  +   strategies <- c(strategies, "batchtools_local")
  + }
  Loading required package: future.batchtools
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'future.batchtools'
  > 
  > message("Future strategies to test: ", paste(sQuote(strategies), collapse=", "))
  Future strategies to test: 'sequential', 'multisession'
  > 
  > fits <- list()
  > for (strategy in strategies) {
  +   message(sprintf("- segmentByCBS() using '%s' futures ...", strategy))
  +   plan(strategy)
  +   fit <- segmentByCBS(data, seed=0xBEEF, verbose=TRUE)
  +   fits[[strategy]] <- fit
  +   stopifnot(all.equal(fit, fits[[1]]))
  + }
  - segmentByCBS() using 'sequential' futures ...
  Segmenting by CBS...
   Segmenting multiple chromosomes...
    Number of chromosomes: 3
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
    Chromosome #1 ('Chr01') of 3...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
    Chromosome #1 ('Chr01') of 3...done
    Chromosome #2 ('Chr02') of 3...
     Segmenting by CBS...
      Chromosome: 2
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
    Chromosome #2 ('Chr02') of 3...done
    Chromosome #3 ('Chr03') of 3...
     Segmenting by CBS...
      Chromosome: 3
       Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
    Chromosome #3 ('Chr03') of 3...done
   Segmenting multiple chromosomes...done
  Segmenting by CBS...done
   list()
  - segmentByCBS() using 'multisession' futures ...
  Segmenting by CBS...
   Segmenting multiple chromosomes...
    Number of chromosomes: 3
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
    Chromosome #1 ('Chr01') of 3...
    Chromosome #1 ('Chr01') of 3...done
    Chromosome #2 ('Chr02') of 3...
    Chromosome #2 ('Chr02') of 3...done
    Chromosome #3 ('Chr03') of 3...
       Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
    Chromosome #3 ('Chr03') of 3...done
     Segmenting by CBS...
      Chromosome: 3
       Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     Segmenting by CBS...done
   Segmenting multiple chromosomes...done
  Segmenting by CBS...done
   list()
  > 
  > 
  > message("*** segmentByCBS() via futures with known segments ...")
  *** segmentByCBS() via futures with known segments ...
  > fits <- list()
  > dataT <- subset(data, chromosome == 1)
  > for (strategy in strategies) {
  +   message(sprintf("- segmentByCBS() w/ known segments using '%s' futures ...", strategy))
  +   plan(strategy)
  +   fit <- segmentByCBS(dataT, knownSegments=knownSegments, seed=0xBEEF, verbose=TRUE)
  +   fits[[strategy]] <- fit
  +   stopifnot(all.equal(fit, fits[[1]]))
  + }
  - segmentByCBS() w/ known segments using 'sequential' futures ...
  Segmenting by CBS...
   Chromosome: 1
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
  Segmenting by CBS...done
   list()
  - segmentByCBS() w/ known segments using 'multisession' futures ...
  Segmenting by CBS...
   Chromosome: 1
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
  Segmenting by CBS...done
   list()
  > 
  > message("*** segmentByCBS() via futures ... DONE")
  *** segmentByCBS() via futures ... DONE
  > 
  > 
  > ## Cleanup
  > plan(oplan)
  > rm(list=c("fits", "dataT", "data", "fit"))
  > 
  > 
  > proc.time()
     user  system elapsed 
     2.29    0.21    6.14 
Running the tests in 'tests/segmentByCBS,prune.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > ## Compare segments
  > assertMatchingSegments <- function(fitM, fit) {
  +   chrs <- getChromosomes(fitM)
  +   segsM <- lapply(chrs, FUN=function(chr) {
  +     getSegments(extractChromosome(fitM, chr))
  +   })
  +   segs <- lapply(fit[chrs], FUN=getSegments)
  +   stopifnot(all.equal(segsM, segs, check.attributes=FALSE))
  + }
  > 
  > ## Simulate data
  > set.seed(0xBEEF)
  > J <- 1000
  > mu <- double(J)
  > mu[200:300] <- mu[200:300] + 1
  > mu[350:400] <- NA
  > mu[650:800] <- mu[650:800] - 1
  > eps <- rnorm(J, sd=1/2)
  > y <- mu + eps
  > x <- sort(runif(length(y), max=length(y))) * 1e5
  > 
  > data <- list()
  > for (chr in 1:2) {
  +   data[[chr]] <- data.frame(chromosome=chr, x=x, y=y)
  + }
  > data$M <- Reduce(rbind, data)
  > 
  > ## Segment
  > message("*** segmentByCBS()")
  *** segmentByCBS()
  > fit <- lapply(data, FUN=segmentByCBS)
  > print(fit)
  [[1]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          1    55167.82 20774251       201  0.0164
  2       <NA>          1 20774250.85 29320105        99  1.0474
  3       <NA>          1 29320104.86 65874675       298 -0.0203
  4       <NA>          1 65874675.06 81348129       151 -1.0813
  5       <NA>          1 81348129.20 99910827       200 -0.0612
  
  [[2]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          2    55167.82 20774251       201  0.0164
  2       <NA>          2 20774250.85 29320105        99  1.0474
  3       <NA>          2 29320104.86 65874675       298 -0.0203
  4       <NA>          2 65874675.06 81348129       151 -1.0813
  5       <NA>          2 81348129.20 99910827       200 -0.0612
  
  $M
     sampleName chromosome       start      end nbrOfLoci    mean
  1        <NA>          1    55167.82 20774251       201  0.0164
  2        <NA>          1 20774250.85 29320105        99  1.0474
  3        <NA>          1 29320104.86 65874675       298 -0.0203
  4        <NA>          1 65874675.06 81348129       151 -1.0813
  5        <NA>          1 81348129.20 99910827       200 -0.0612
  6        <NA>         NA          NA       NA        NA      NA
  7        <NA>          2    55167.82 20774251       201  0.0164
  8        <NA>          2 20774250.85 29320105        99  1.0474
  9        <NA>          2 29320104.86 65874675       298 -0.0203
  10       <NA>          2 65874675.06 81348129       151 -1.0813
  11       <NA>          2 81348129.20 99910827       200 -0.0612
  
  > assertMatchingSegments(fit$M, fit)
  > 
  > ## Join segments
  > message("*** joinSegments()")
  *** joinSegments()
  > fitj <- lapply(fit, FUN=joinSegments)
  > print(fitj)
  [[1]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          1    55167.82 20774251       201  0.0164
  2       <NA>          1 20774250.85 29320105        99  1.0474
  3       <NA>          1 29320104.86 65874675       298 -0.0203
  4       <NA>          1 65874675.06 81348129       151 -1.0813
  5       <NA>          1 81348129.20 99910827       200 -0.0612
  
  [[2]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          2    55167.82 20774251       201  0.0164
  2       <NA>          2 20774250.85 29320105        99  1.0474
  3       <NA>          2 29320104.86 65874675       298 -0.0203
  4       <NA>          2 65874675.06 81348129       151 -1.0813
  5       <NA>          2 81348129.20 99910827       200 -0.0612
  
  $M
     sampleName chromosome       start      end nbrOfLoci    mean
  1        <NA>          1    55167.82 20774251       201  0.0164
  2        <NA>          1 20774250.85 29320105        99  1.0474
  3        <NA>          1 29320104.86 65874675       298 -0.0203
  4        <NA>          1 65874675.06 81348129       151 -1.0813
  5        <NA>          1 81348129.20 99910827       200 -0.0612
  6        <NA>         NA          NA       NA        NA      NA
  7        <NA>          2    55167.82 20774251       201  0.0164
  8        <NA>          2 20774250.85 29320105        99  1.0474
  9        <NA>          2 29320104.86 65874675       298 -0.0203
  10       <NA>          2 65874675.06 81348129       151 -1.0813
  11       <NA>          2 81348129.20 99910827       200 -0.0612
  
  > assertMatchingSegments(fitj$M, fitj)
  > 
  > ## Reset segments
  > message("*** resetSegments()")
  *** resetSegments()
  > fitj <- lapply(fit, FUN=resetSegments)
  > print(fitj)
  [[1]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          1    55167.82 20774251       201  0.0164
  2       <NA>          1 20774250.85 29320105        99  1.0474
  3       <NA>          1 29320104.86 65874675       298 -0.0203
  4       <NA>          1 65874675.06 81348129       151 -1.0813
  5       <NA>          1 81348129.20 99910827       200 -0.0612
  
  [[2]]
    sampleName chromosome       start      end nbrOfLoci    mean
  1       <NA>          2    55167.82 20774251       201  0.0164
  2       <NA>          2 20774250.85 29320105        99  1.0474
  3       <NA>          2 29320104.86 65874675       298 -0.0203
  4       <NA>          2 65874675.06 81348129       151 -1.0813
  5       <NA>          2 81348129.20 99910827       200 -0.0612
  
  $M
     sampleName chromosome       start      end nbrOfLoci    mean
  1        <NA>          1    55167.82 20774251       201  0.0164
  2        <NA>          1 20774250.85 29320105        99  1.0474
  3        <NA>          1 29320104.86 65874675       298 -0.0203
  4        <NA>          1 65874675.06 81348129       151 -1.0813
  5        <NA>          1 81348129.20 99910827       200 -0.0612
  6        <NA>         NA          NA       NA        NA      NA
  7        <NA>          2    55167.82 20774251       201  0.0164
  8        <NA>          2 20774250.85 29320105        99  1.0474
  9        <NA>          2 29320104.86 65874675       298 -0.0203
  10       <NA>          2 65874675.06 81348129       151 -1.0813
  11       <NA>          2 81348129.20 99910827       200 -0.0612
  
  > assertMatchingSegments(fitj$M, fitj)
  > 
  > ## Prune by SD undo
  > message("*** pruneBySdUndo()")
  *** pruneBySdUndo()
  > fitp <- lapply(fit, FUN=pruneBySdUndo)
  > print(fitp)
  [[1]]
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          1 55167.82 99910827       949 -0.07857059
  
  [[2]]
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          2 55167.82 99910827       949 -0.07857059
  
  $M
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          1 55167.82 99910827       949 -0.07857059
  2       <NA>         NA       NA       NA        NA          NA
  3       <NA>          2 55167.82 99910827       949 -0.07857059
  
  > assertMatchingSegments(fitp$M, fitp)
  > 
  > ## Prune by hierarchical clustering
  > message("*** pruneByHClust()")
  *** pruneByHClust()
  > fitp <- lapply(fit, FUN=pruneByHClust, k=1L)
  > print(fitp)
  [[1]]
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          1 55167.82 99910827       949 -0.07857059
  
  [[2]]
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          2 55167.82 99910827       949 -0.07857059
  
  $M
    sampleName chromosome    start      end nbrOfLoci        mean
  1       <NA>          1 55167.82 99910827       949 -0.07857059
  6       <NA>         NA       NA       NA        NA          NA
  7       <NA>          2 55167.82 99910827       949 -0.07857059
  
  > assertMatchingSegments(fitp$M, fitp)
  > 
  > proc.time()
     user  system elapsed 
     1.29    0.20    1.71 
Running the tests in 'tests/segmentByCBS,report.R' failed.
Complete output:
  > # This test script calls a report generator which requires
  > # the 'ggplot2' package, which in turn will require packages
  > # 'colorspace', 'dichromat', 'munsell', 'reshape2' and 'scales'.
  > 
  > # Only run this test in full testing mode
  > if (Sys.getenv("_R_CHECK_FULL_") != "") {
  + library("PSCBS")
  + 
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # Load SNP microarray data
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + data <- PSCBS::exampleData("paired.chr01")
  + str(data)
  + 
  + data <- data.frame(chromosome=data$chromosome, x=data$x, y=data$CT)
  + 
  + 
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # CBS segmentation
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # Drop single-locus outliers
  + dataS <- dropSegmentationOutliers(data)
  + 
  + # Speed up example by segmenting fewer loci
  + dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  + 
  + str(dataS)
  + 
  + gaps <- findLargeGaps(dataS, minLength=2e6)
  + knownSegments <- gapsToSegments(gaps)
  + 
  + # CBS segmentation
  + fit <- segmentByCBS(dataS, knownSegments=knownSegments,
  +                             seed=0xBEEF, verbose=-10)
  + signalType(fit) <- "ratio"
  + 
  + # Fake a multi-chromosome segmentation
  + fit1 <- fit
  + fit2 <- renameChromosomes(fit, from=1, to=2)
  + fit <- c(fit1, fit2)
  + 
  + report(fit, sampleName="CBS", studyName="CBS-Ex", verbose=-10)
  + 
  + } # if (Sys.getenv("_R_CHECK_FULL_"))
  > 
  > proc.time()
     user  system elapsed 
     0.14    0.09    0.18 
Running the tests in 'tests/segmentByCBS,shiftTCN.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > subplots <- R.utils::subplots
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Simulating copy-number data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > set.seed(0xBEEF)
  > 
  > # Number of loci
  > J <- 1000
  > 
  > mu <- double(J)
  > eps <- rnorm(J, sd=1/2)
  > y <- mu + eps
  > x <- sort(runif(length(y), max=length(y)))
  > 
  > idxs <- which(200 <= x & x < 300)
  > y[idxs] <- y[idxs] + 1
  > idxs <- which(350 <= x & x < 400)
  > y[idxs] <- NA # centromere
  > x[idxs] <- NA # centromere
  > idxs <- which(650 <= x & x < 800)
  > y[idxs] <- y[idxs] - 1
  > x <- x*1e5
  > 
  > keep <- is.finite(x)
  > x <- x[keep]
  > y <- y[keep]
  > 
  > data <- list()
  > for (chr in 1:2) {
  +   data[[chr]] <- data.frame(chromosome=chr, y=y, x=x)
  + }
  > data <- Reduce(rbind, data)
  > 
  > 
  > subplots(7, ncol=1)
  > par(mar=c(1.7,1,0.2,1)+0.1)
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- segmentByCBS(data)
  > print(fit)
     sampleName chromosome       start      end nbrOfLoci    mean
  1        <NA>          1    55167.82 20341782       195  0.0145
  2        <NA>          1 20341781.95 29617861       108  1.0437
  3        <NA>          1 29617861.37 64995303       299 -0.0208
  4        <NA>          1 64995302.97 80042680       151 -1.0700
  5        <NA>          1 80042679.86 99910827       211 -0.0568
  6        <NA>         NA          NA       NA        NA      NA
  7        <NA>          2    55167.82 20341782       195  0.0145
  8        <NA>          2 20341781.95 29617861       108  1.0437
  9        <NA>          2 29617861.37 64995303       299 -0.0208
  10       <NA>          2 64995302.97 80042680       151 -1.0700
  11       <NA>          2 80042679.86 99910827       211 -0.0568
  > 
  > Clim <- c(-3,3) + c(0,10)
  > plotTracks(fit, Clim=Clim)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Shifting every other chromosome
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fitList <- list()
  > chrs <- getChromosomes(fit)
  > for (kk in seq_along(chrs)) {
  +   chr <- chrs[kk]
  +   fitKK <- extractChromosome(fit, chr)
  +   if (kk %% 2 == 0) {
  +     fitKK <- shiftTCN(fitKK, shift=+10)
  +   }
  +   fitList[[kk]] <- fitKK
  + } # for (kk ...)
  > fitT <- do.call(c, fitList)
  > # Sanity check
  > stopifnot(nbrOfSegments(fitT) == nbrOfSegments(fit))
  > 
  > plotTracks(fitT, Clim=Clim)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Shifting every other known segment
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > gaps <- findLargeGaps(data, minLength=40e5)
  > knownSegments <- gapsToSegments(gaps, dropGaps=TRUE)
  > fit <- segmentByCBS(data, knownSegments=knownSegments)
  > 
  > subplots(2, ncol=1)
  > plotTracks(fit, Clim=Clim)
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > fitList <- list()
  > for (kk in seq_len(nrow(knownSegments))) {
  +   seg <- knownSegments[kk,]
  +   start <- seg$start
  +   end <- seg$end
  +   fitKK <- extractChromosome(fit, seg$chromosome)
  +   segsKK <- getSegments(fitKK)
  +   idxStart <- min(which(segsKK$start >= start))
  +   idxEnd <- max(which(segsKK$end <= end))
  +   idxs <- idxStart:idxEnd
  +   fitKK <- extractSegments(fitKK, idxs)
  +   if (kk %% 2 == 0) {
  +     fitKK <- shiftTCN(fitKK, shift=+10)
  +   }
  +   fitList[[kk]] <- fitKK
  + } # for (kk ...)
  > fitT <- do.call(c, fitList)
  > # Sanity check
  > stopifnot(nbrOfSegments(fitT) == nbrOfSegments(fit))
  > 
  > plotTracks(fitT, Clim=Clim)
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > segList <- seqOfSegmentsByDP(fit)
  > K <- length(segList)
  > subplots(K, ncol=2, byrow=FALSE)
  > par(mar=c(2,1,1,1))
  > for (kk in 1:K) {
  +   knownSegments <- segList[[kk]]
  +   fitKK <- resegment(fit, knownSegments=knownSegments, undo=+Inf)
  +   plotTracks(fitKK, Clim=c(-3,3))
  + } # for (kk ...)
  > 
  > proc.time()
     user  system elapsed 
    10.01    0.54   10.76 
Running the tests in 'tests/segmentByCBS,weights.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Simulating copy-number data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > set.seed(0xBEEF)
  > 
  > # Number of loci
  > J <- 1000
  > 
  > x <- sort(runif(J, max=J)) * 1e5
  > 
  > mu <- double(J)
  > mu[200:300] <- mu[200:300] + 1
  > mu[350:400] <- NA # centromere
  > mu[650:800] <- mu[650:800] - 1
  > eps <- rnorm(J, sd=1/2)
  > y <- mu + eps
  > 
  > outliers <- seq(from=1L, to=J, length.out=0.2*J)
  > y[outliers] <- y[outliers] + 1.5
  > 
  > w <- rep(1.0, times=J)
  > w[outliers] <- 0.01
  > 
  > data <- data.frame(chromosome=1L, x=x, y=y)
  > dataW <- cbind(data, w=w)
  > 
  > 
  > par(mar=c(2,3,0.2,1)+0.1)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Single-chromosome segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segment without weights
  > fit <- segmentByCBS(data)
  > sampleName(fit) <- "CBS_Example"
  > print(fit)
     sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example          1   136857.7 19138391       199  0.2712
  2 CBS_Example          1 19138391.4 28682180       101  1.2168
  3 CBS_Example          1 28682180.1 64690253       298  0.3027
  4 CBS_Example          1 64690253.3 80738828       151 -0.7101
  5 CBS_Example          1 80738828.3 99932904       200  0.3655
  > plotTracks(fit)
  Warning message:
  In plotTracks.CBS(fit) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit) is unknown ('NA'). Use signalType(fit) <- 'ratio' to avoid this warning.
  > ## Highlight outliers (they pull up the mean levels)
  > points(x[outliers]/1e6, y[outliers], col="purple")
  > 
  > # Segment with weights
  > fitW <- segmentByCBS(dataW)
  > sampleName(fitW) <- "CBS_Example (weighted)"
  > print(fitW)
                sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example (weighted)          1   136857.7 19138391       199 -0.0041
  2 CBS_Example (weighted)          1 19138391.4 28682180       101  0.8987
  3 CBS_Example (weighted)          1 28682180.1 64690253       298  0.0159
  4 CBS_Example (weighted)          1 64690253.3 80738828       151 -1.0215
  5 CBS_Example (weighted)          1 80738828.3 99932904       200  0.0653
  > drawLevels(fitW, col="red")
  NULL
  > 
  > legend("topright", bg="white", legend=c("outliers", "non-weighted CBS", "weighted CBS"), col=c("purple", "purple", "red"), lwd=c(NA,3,3), pch=c(1,NA,NA))
  > 
  > ## Assert that weighted segment means are less biased
  > dmean <- getSegments(fit)$mean - getSegments(fitW)$mean
  > cat("Segment mean differences:\n")
  Segment mean differences:
  > print(dmean)
  [1] 0.2753 0.3181 0.2868 0.3114 0.3002
  > stopifnot(all(dmean > 0, na.rm=TRUE))
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segmentation with some known change points
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > knownSegments <- data.frame(
  +   chromosome=c(    1,   1),
  +   start     =x[c(  1, 401)],
  +   end       =x[c(349,   J)]
  + )
  > fit2 <- segmentByCBS(dataW, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit2) <- "CBS_Example_2 (weighted)"
  > print(fit2)
                  sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example_2 (weighted)          1   136857.7 19138391       199 -0.0041
  2 CBS_Example_2 (weighted)          1 19138391.4 28682180       101  0.8987
  3 CBS_Example_2 (weighted)          1 28682180.1 34062461        49 -0.0552
  4 CBS_Example_2 (weighted)          1 38343432.8 64690253       249  0.0298
  5 CBS_Example_2 (weighted)          1 64690253.3 80738828       151 -1.0215
  6 CBS_Example_2 (weighted)          1 80738828.3 99932904       200  0.0653
  > plotTracks(fit2)
  Warning message:
  In plotTracks.CBS(fit2) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit2) is unknown ('NA'). Use signalType(fit2) <- 'ratio' to avoid this warning.
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > # Chromosome boundaries can be specified as -Inf and +Inf
  > knownSegments <- data.frame(
  +   chromosome=c(     1,      1),
  +   start     =c(  -Inf, x[401]),
  +   end       =c(x[349],   +Inf)
  + )
  > fit2b <- segmentByCBS(dataW, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit2b) <- "CBS_Example_2b (weighted)"
  > print(fit2b)
                   sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example_2b (weighted)          1   136857.7 19138391       199 -0.0041
  2 CBS_Example_2b (weighted)          1 19138391.4 28682180       101  0.8987
  3 CBS_Example_2b (weighted)          1 28682180.1 34062461        49 -0.0552
  4 CBS_Example_2b (weighted)          1 38343432.8 64690253       249  0.0298
  5 CBS_Example_2b (weighted)          1 64690253.3 80738828       151 -1.0215
  6 CBS_Example_2b (weighted)          1 80738828.3 99932904       200  0.0653
  > plotTracks(fit2b)
  Warning message:
  In plotTracks.CBS(fit2b) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit2b) is unknown ('NA'). Use signalType(fit2b) <- 'ratio' to avoid this warning.
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > # As a proof of concept, it is possible to segment just the centromere,
  > # which contains no data.  All statistics will be NAs.
  > knownSegments <- data.frame(
  +   chromosome=c(    1),
  +   start     =x[c(350)],
  +   end       =x[c(400)]
  + )
  > fit3 <- segmentByCBS(dataW, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit3) <- "CBS_Example_3"
  > print(fit3)
       sampleName chromosome    start      end nbrOfLoci mean
  1 CBS_Example_3          1 34108010 38257409         0   NA
  > plotTracks(fit3, Clim=c(0,5), xlim=c(0,100))
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > # If one specify the (empty) centromere as a segment, then its
  > # estimated statistics will be NAs, which becomes a natural
  > # separator between the two "independent" arms.
  > knownSegments <- data.frame(
  +   chromosome=c(    1,   1,   1),
  +   start     =x[c(  1, 350, 401)],
  +   end       =x[c(349, 400,   J)]
  + )
  > fit4 <- segmentByCBS(dataW, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit4) <- "CBS_Example_4"
  > print(fit4)
       sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example_4          1   136857.7 19138391       199 -0.0041
  2 CBS_Example_4          1 19138391.4 28682180       101  0.8987
  3 CBS_Example_4          1 28682180.1 34062461        49 -0.0552
  4 CBS_Example_4          1 34108009.8 38257409         0      NA
  5 CBS_Example_4          1 38343432.8 64690253       249  0.0298
  6 CBS_Example_4          1 64690253.3 80738828       151 -1.0215
  7 CBS_Example_4          1 80738828.3 99932904       200  0.0653
  > plotTracks(fit4)
  Warning message:
  In plotTracks.CBS(fit4) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit4) is unknown ('NA'). Use signalType(fit4) <- 'ratio' to avoid this warning.
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > fit5 <- segmentByCBS(dataW, knownSegments=knownSegments, undo=Inf, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit5) <- "CBS_Example_5"
  > print(fit5)
       sampleName chromosome      start      end nbrOfLoci       mean
  1 CBS_Example_5          1   136857.7 34062461       349 0.54781248
  2 CBS_Example_5          1 34108009.8 38257409         0         NA
  3 CBS_Example_5          1 38343432.8 99932904       600 0.06959745
  > plotTracks(fit5)
  Warning message:
  In plotTracks.CBS(fit5) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit5) is unknown ('NA'). Use signalType(fit5) <- 'ratio' to avoid this warning.
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > stopifnot(nbrOfSegments(fit5) == nrow(knownSegments))
  > 
  > 
  > # One can also force a separator between two segments by setting
  > # 'start' and 'end' to NAs ('chromosome' has to be given)
  > knownSegments <- data.frame(
  +   chromosome=c(    1,  1,   1),
  +   start     =x[c(  1, NA, 401)],
  +   end       =x[c(349, NA,   J)]
  + )
  > fit6 <- segmentByCBS(dataW, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 1
  Segmenting by CBS...done
  > sampleName(fit6) <- "CBS_Example_6"
  > print(fit6)
       sampleName chromosome      start      end nbrOfLoci    mean
  1 CBS_Example_6          1   136857.7 19138391       199 -0.0041
  2 CBS_Example_6          1 19138391.4 28682180       101  0.8987
  3 CBS_Example_6          1 28682180.1 34062461        49 -0.0552
  4          <NA>         NA         NA       NA        NA      NA
  5 CBS_Example_6          1 38343432.8 64690253       249  0.0298
  6 CBS_Example_6          1 64690253.3 80738828       151 -1.0215
  7 CBS_Example_6          1 80738828.3 99932904       200  0.0653
  > plotTracks(fit6)
  Warning message:
  In plotTracks.CBS(fit6) :
    Setting default 'Clim' assuming the signal type is 'ratio' because signalType(fit6) is unknown ('NA'). Use signalType(fit6) <- 'ratio' to avoid this warning.
  > abline(v=c(knownSegments$start, knownSegments$end)/1e6, lty=3)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Multi-chromosome segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data2 <- data
  > data2$chromosome <- 2L
  > data <- rbind(data, data2)
  > dataW <- cbind(data, w=w)
  > 
  > par(mar=c(2,3,0.2,1)+0.1)
  > # Segment without weights
  > fit <- segmentByCBS(data)
  > sampleName(fit) <- "CBS_Example"
  > print(fit)
      sampleName chromosome      start      end nbrOfLoci    mean
  1  CBS_Example          1   136857.7 19138391       199  0.2712
  2  CBS_Example          1 19138391.4 28682180       101  1.2168
  3  CBS_Example          1 28682180.1 64690253       298  0.3027
  4  CBS_Example          1 64690253.3 80738828       151 -0.7101
  5  CBS_Example          1 80738828.3 99932904       200  0.3655
  6         <NA>         NA         NA       NA        NA      NA
  7  CBS_Example          2   136857.7 19138391       199  0.2712
  8  CBS_Example          2 19138391.4 28682180       101  1.2168
  9  CBS_Example          2 28682180.1 64690253       298  0.3027
  10 CBS_Example          2 64690253.3 80738828       151 -0.7101
  11 CBS_Example          2 80738828.3 99932904       200  0.3655
  > plotTracks(fit, Clim=c(-3,3))
  > 
  > # Segment with weights
  > fitW <- segmentByCBS(dataW)
  > sampleName(fitW) <- "CBS_Example (weighted)"
  > print(fitW)
                 sampleName chromosome      start      end nbrOfLoci    mean
  1  CBS_Example (weighted)          1   136857.7 19138391       199 -0.0041
  2  CBS_Example (weighted)          1 19138391.4 28682180       101  0.8987
  3  CBS_Example (weighted)          1 28682180.1 64690253       298  0.0159
  4  CBS_Example (weighted)          1 64690253.3 80738828       151 -1.0215
  5  CBS_Example (weighted)          1 80738828.3 99932904       200  0.0653
  6                    <NA>         NA         NA       NA        NA      NA
  7  CBS_Example (weighted)          2   136857.7 19138391       199 -0.0041
  8  CBS_Example (weighted)          2 19138391.4 28682180       101  0.8987
  9  CBS_Example (weighted)          2 28682180.1 64690253       298  0.0159
  10 CBS_Example (weighted)          2 64690253.3 80738828       151 -1.0215
  11 CBS_Example (weighted)          2 80738828.3 99932904       200  0.0653
  > drawLevels(fitW, col="red")
  NULL
  > 
  > legend("topright", bg="white", legend=c("outliers", "non-weighted CBS", "weighted CBS"), col=c("purple", "purple", "red"), lwd=c(NA,3,3), pch=c(1,NA,NA))
  > 
  > ## Assert that weighted segment means are less biased
  > dmean <- getSegments(fit)$mean - getSegments(fitW)$mean
  > cat("Segment mean differences:\n")
  Segment mean differences:
  > print(dmean)
   [1] 0.2753 0.3181 0.2868 0.3114 0.3002     NA 0.2753 0.3181 0.2868 0.3114
  [11] 0.3002
  > stopifnot(all(dmean > 0, na.rm=TRUE))
  > 
  > proc.time()
     user  system elapsed 
     3.54    0.32    4.03 
Running the tests in 'tests/segmentByPairedPSCBS,DH.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Load SNP microarray data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data <- PSCBS::exampleData("paired.chr01")
  > str(data)
  'data.frame':	73346 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2224111 2319424 2543484 2926730 2941694 3084986 3155127 3292731 3695086 ...
   $ CT        : num  1.625 1.071 1.406 1.18 0.856 ...
   $ betaT     : num  0.757 0.771 0.834 0.778 0.229 ...
   $ CN        : num  2.36 2.13 2.59 1.93 1.71 ...
   $ betaN     : num  0.827 0.875 0.887 0.884 0.103 ...
  > 
  > # Drop single-locus outliers
  > dataS <- dropSegmentationOutliers(data)
  > 
  > # Run light-weight tests
  > # Use only every 5th data point
  > dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  > # Number of segments (for assertion)
  > nSegs <- 3L
  > # Number of bootstrap samples (see below)
  > B <- 100L
  > 
  > str(dataS)
  'data.frame':	14670 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2941694 3710825 4240737 4276892 4464544 4714611 5095111 5034491 5158416 ...
   $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
   $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
   $ CN        : num  2.36 2.13 2.26 2.01 2.32 ...
   $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
  > R.oo::attachLocally(dataS)
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calculate DH
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > muN <- aroma.light::callNaiveGenotypes(betaN, censorAt=c(0,1))
  > # SNPs are identifies as those loci that have non-missing 'betaT' & 'muN'
  > isSnp <- (!is.na(betaT) & !is.na(muN))
  > isHet <- isSnp & (muN == 1/2)
  > rho <- rep(NA_real_, length=length(muN))
  > rho[isHet] <- 2*abs(betaT[isHet]-1/2)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Paired PSCBS segmentation using TCN and DH only
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- segmentByPairedPSCBS(CT, rho=rho,
  +                             chromosome=chromosome, x=x,
  +                             seed=0xBEEF, verbose=-10)
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Setup up data...
    'data.frame':	14670 obs. of  4 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ rho       : num  NA 0.662 NA NA NA ...
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 12
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	14658 obs. of  4 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ rho       : num  NA NA NA NA NA ...
   Ordering data along genome...done
   Keeping only current chromosome for 'knownSegments'...
    Chromosome: 1
    Known segments for this chromosome:
    [1] chromosome start      end       
    <0 rows> (or 0-length row.names)
   Keeping only current chromosome for 'knownSegments'...done
   alphaTCN: 0.009
   alphaDH: 0.001
   Number of loci: 14658
   Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
   Produced 2 seeds from this stream for future usage
   Identification of change points by total copy numbers...
    Segmenting by CBS...
     Chromosome: 1
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
    Segmenting by CBS...done
    List of 4
     $ data   :'data.frame':	14658 obs. of  4 variables:
      ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
      ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
      ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
      ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
     $ output :'data.frame':	3 obs. of  6 variables:
      ..$ sampleName: chr [1:3] NA NA NA
      ..$ chromosome: int [1:3] 1 1 1
      ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
      ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
      ..$ nbrOfLoci : int [1:3] 7599 2668 4391
      ..$ mean      : num [1:3] 1.39 2.07 2.63
     $ segRows:'data.frame':	3 obs. of  2 variables:
      ..$ startRow: int [1:3] 1 7600 10268
      ..$ endRow  : int [1:3] 7599 10267 14658
     $ params :List of 5
      ..$ alpha        : num 0.009
      ..$ undo         : num 0
      ..$ joinSegments : logi TRUE
      ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
      .. ..$ chromosome: int 1
      .. ..$ start     : num -Inf
      .. ..$ end       : num Inf
      ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
     - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
     - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.72 0.01 0.73 NA NA
      ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
     - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
     - attr(*, "randomSeed")= int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
   Identification of change points by total copy numbers...done
   Restructure TCN segmentation results...
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
    1          1    554484 143926517         7599  1.3859
    2          1 143926517 185449813         2668  2.0704
    3          1 185449813 247137334         4391  2.6341
    Number of TCN segments: 3
   Restructure TCN segmentation results...done
   Total CN segment #1 ([    554484,1.43927e+08]) of 3...
    Number of TCN loci in segment: 7599
    Locus data for TCN segment:
    'data.frame':	7599 obs. of  5 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ rho       : num  NA NA NA NA NA ...
     $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
    Number of loci: 7599
    Number of SNPs: 2111 (27.78%)
    Number of heterozygous SNPs: 2111 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	7599 obs. of  4 variables:
       ..$ chromosome: int [1:7599] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
       ..$ y         : num [1:7599] NA NA NA NA NA ...
       ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 554484
       ..$ end       : num 1.44e+08
       ..$ nbrOfLoci : int 2111
       ..$ mean      : num 0.524
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 10
       ..$ endRow  : int 7594
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 554484
       .. ..$ end       : num 1.44e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.06 0 0.06 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       10   7594
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     DH segmentation rows:
       startRow endRow
     1       10   7594
    Segmenting DH signals...done
    DH segmentation table:
      dhStart     dhEnd dhNbrOfLoci dhMean
    1  554484 143926517        2111 0.5237
      startRow endRow
    1       10   7594
    Rows:
    [1] 1
    TCN segmentation rows:
      startRow endRow
    1        1   7599
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
      startRow endRow
    1       10   7594
    NULL
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
      startRow endRow
    1        1   7599
    Total CN segmentation table (expanded):
      chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    1          1   554484 143926517         7599  1.3859         2111         2111
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1     1    1          1   554484 143926517         7599  1.3859         2111
      tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
    1         2111  554484 143926517        2111 0.5237
   Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
   Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
    Number of TCN loci in segment: 2668
    Locus data for TCN segment:
    'data.frame':	2668 obs. of  5 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
     $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
     $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
     $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
    Number of loci: 2668
    Number of SNPs: 774 (29.01%)
    Number of heterozygous SNPs: 774 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	2668 obs. of  4 variables:
       ..$ chromosome: int [1:2668] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
       ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
       ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.44e+08
       ..$ end       : num 1.85e+08
       ..$ nbrOfLoci : int 774
       ..$ mean      : num 0.154
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 15
       ..$ endRow  : int 2664
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.44e+08
       .. ..$ end       : num 1.85e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.02 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       15   2664
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     DH segmentation rows:
       startRow endRow
     1     7614  10263
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 143926517 185449813         774 0.1542
      startRow endRow
    1     7614  10263
    Rows:
    [1] 2
    TCN segmentation rows:
      startRow endRow
    2     7600  10267
    TCN and DH segmentation rows:
      startRow endRow
    2     7600  10267
      startRow endRow
    1     7614  10263
      startRow endRow
    1        1   7599
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
    2     7614  10263
      startRow endRow
    1        1   7599
    2     7600  10267
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    2          1 143926517 185449813         2668  2.0704          774          774
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    2     2    1          1 143926517 185449813         2668  2.0704          774
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    2          774 143926517 185449813         774 0.1542
   Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
   Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
    Number of TCN loci in segment: 4391
    Locus data for TCN segment:
    'data.frame':	4391 obs. of  5 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
     $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
     $ rho       : num  NA 0.0308 NA 0.2533 NA ...
     $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
    Number of loci: 4391
    Number of SNPs: 1311 (29.86%)
    Number of heterozygous SNPs: 1311 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	4391 obs. of  4 variables:
       ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
       ..$ y         : num [1:4391] NA 0.0308 NA 0.2533 NA ...
       ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.85e+08
       ..$ end       : num 2.47e+08
       ..$ nbrOfLoci : int 1311
       ..$ mean      : num 0.251
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 2
       ..$ endRow  : int 4388
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.85e+08
       .. ..$ end       : num 2.47e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.04 0 0.03 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        2   4388
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     DH segmentation rows:
       startRow endRow
     1    10269  14655
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 185449813 247137334        1311 0.2512
      startRow endRow
    1    10269  14655
    Rows:
    [1] 3
    TCN segmentation rows:
      startRow endRow
    3    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    3    10268  14658
      startRow endRow
    1    10269  14655
      startRow endRow
    1        1   7599
    2     7600  10267
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
    2     7614  10263
    3    10269  14655
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    3          1 185449813 247137334         4391  2.6341         1311         1311
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    3     3    1          1 185449813 247137334         4391  2.6341         1311
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    3         1311 185449813 247137334        1311 0.2512
   Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
     chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
   1          1     1    1    554484 143926517         7599  1.3859         2111
   2          1     2    1 143926517 185449813         2668  2.0704          774
   3          1     3    1 185449813 247137334         4391  2.6341         1311
     tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
   1         2111    554484 143926517        2111 0.5237
   2          774 143926517 185449813         774 0.1542
   3         1311 185449813 247137334        1311 0.2512
   Calculating (C1,C2) per segment...
   Calculating (C1,C2) per segment...done
   Number of segments: 3
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Post-segmenting TCNs...
   Number of segments: 3
   Number of chromosomes: 1
   [1] 1
   Chromosome 1 ('chr01') of 1...
    Rows:
    [1] 1 2 3
    Number of segments: 3
    TCN segment #1 ('1') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #1 ('1') of 3...done
    TCN segment #2 ('2') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #2 ('2') of 3...done
    TCN segment #3 ('3') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #3 ('3') of 3...done
   Chromosome 1 ('chr01') of 1...done
   Update (C1,C2) per segment...
   Update (C1,C2) per segment...done
  Post-segmenting TCNs...done
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111    554484 143926517        2111 0.5237 0.3300521 1.055848
  2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
  3         1311 185449813 247137334        1311 0.2512 0.9862070 1.647893
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111    554484 143926517        2111 0.5237 0.3300521 1.055848
  2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
  3         1311 185449813 247137334        1311 0.2512 0.9862070 1.647893
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111        2111 0.5237 0.3300521 1.055848
  2          774         774 0.1542 0.8755722 1.194828
  3         1311        1311 0.2512 0.9862070 1.647893
  > 
  > # Plot results
  > plotTracks(fit)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Bootstrap segment level estimates
  > # (used by the AB caller, which, if skipped here,
  > #  will do it automatically)
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- bootstrapTCNandDHByRegion(fit, B=B, verbose=-10)
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...
   Already done?
    tcn_2.5%    tcn_5%   tcn_95% tcn_97.5%   dh_2.5%     dh_5%    dh_95%  dh_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
     c1_2.5%     c1_5%    c1_95%  c1_97.5%   c2_2.5%     c2_5%    c2_95%  c2_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...
     Number of loci: 14658
     Number of SNPs: 4196
     Number of non-SNPs: 10462
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...done
     num [1:3, 1:100, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Segment #1 (chr 1, tcnId=1, dhId=1) of 3...
       chromosome tcnId dhId tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1   554484 143926517         7599  1.3859         2111
       tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2111  554484 143926517        2111 0.5237 0.3300521 1.055848
     Number of TCNs: 7599
     Number of DHs: 2111
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
      Non-polymorphic loci:
       int [1:5488] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs to resample for TCN:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:5488] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (2111,0,5488)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #1 (chr 1, tcnId=1, dhId=1) of 3...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 3...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     2          1     2    1 143926517 185449813         2668  2.0704          774
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
     Number of TCNs: 2668
     Number of DHs: 774
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
      Non-polymorphic loci:
       int [1:1894] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      Heterozygous SNPs to resample for TCN:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:1894] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (774,0,1894)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 3...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 3...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     3          1     3    1 185449813 247137334         4391  2.6341         1311
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean   c1Mean   c2Mean
     3         1311 185449813 247137334        1311 0.2512 0.986207 1.647893
     Number of TCNs: 4391
     Number of DHs: 1311
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Non-polymorphic loci:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs to resample for TCN:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (1311,0,3080)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 3...done
    Bootstrapped segment mean levels
     num [1:3, 1:100, 1:4] 1.38 2.08 2.63 1.38 2.07 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...
      num [1:3, 1:100, 1:4] 1.38 2.08 2.63 1.38 2.07 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...done
    Calculating polar (alpha,radius,manhattan) for change points...
      num [1:2, 1:100, 1:2] -0.5588 -0.0962 -0.5365 -0.1285 -0.5378 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:2] "c1" "c2"
     Bootstrapped change points
      num [1:2, 1:100, 1:5] -2.89 -1.78 -2.87 -1.86 -2.88 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Calculating polar (alpha,radius,manhattan) for change points...done
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...
     num [1:3, 1:4, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Field #1 ('tcn') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #1 ('tcn') of 4...done
    Field #2 ('dh') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #2 ('dh') of 4...done
    Field #3 ('c1') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #3 ('c1') of 4...done
    Field #4 ('c2') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #4 ('c2') of 4...done
    Bootstrap statistics
     num [1:3, 1:4, 1:4] 1.38 2.06 2.62 1.38 2.06 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Statistical sanity checks (iff B >= 100)...
     Available summaries: 2.5%, 5%, 95%, 97.5%
     Available quantiles: 0.025, 0.05, 0.95, 0.975
      num [1:3, 1:4, 1:4] 1.38 2.06 2.62 1.38 2.06 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
     Field #1 ('tcn') of 4...
      Seg   1. mean=1.3859, range=[1.38092,1.3949], n=7599
      Seg   2. mean=2.0704, range=[2.05747,2.08326], n=2668
      Seg   3. mean=2.6341, range=[2.62068,2.64694], n=4391
     Field #1 ('tcn') of 4...done
     Field #2 ('dh') of 4...
      Seg   1. mean=0.5237, range=[0.51753,0.532002], n=2111
      Seg   2. mean=0.1542, range=[0.144468,0.16453], n=774
      Seg   3. mean=0.2512, range=[0.242575,0.258832], n=1311
     Field #2 ('dh') of 4...done
     Field #3 ('c1') of 4...
      Seg   1. mean=0.330052, range=[0.323996,0.336038], n=2111
      Seg   2. mean=0.875572, range=[0.86318,0.887699], n=774
      Seg   3. mean=0.986207, range=[0.975123,0.998982], n=1311
     Field #3 ('c1') of 4...done
     Field #4 ('c2') of 4...
      Seg   1. mean=1.05585, range=[1.05006,1.06231], n=2111
      Seg   2. mean=1.19483, range=[1.18417,1.2081], n=774
      Seg   3. mean=1.64789, range=[1.63403,1.66098], n=1311
     Field #4 ('c2') of 4...done
    Statistical sanity checks (iff B >= 100)...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...done
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...
     num [1:2, 1:4, 1:5] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Field #1 ('alpha') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #1 ('alpha') of 5...done
    Field #2 ('radius') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #2 ('radius') of 5...done
    Field #3 ('manhattan') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #3 ('manhattan') of 5...done
    Field #4 ('d1') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #4 ('d1') of 5...done
    Field #5 ('d2') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #5 ('d2') of 5...done
    Bootstrap statistics
     num [1:2, 1:4, 1:5] -2.92 -1.86 -2.91 -1.85 -2.87 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...done
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111        2111 0.5237 0.3300521 1.055848
  2          774         774 0.1542 0.8755722 1.194828
  3         1311        1311 0.2512 0.9862070 1.647893
  > plotTracks(fit)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments in allelic balance (AB) and
  > # in loss-of-heterozygosity (LOH)
  > # NOTE: Ideally, this should be done on whole-genome data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- callAB(fit, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in DH): 0.3466649145302
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called allelic balance (AB): 2 (66.67%) of 3
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > fit <- callLOH(fit, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in C1): 0.771236438183453
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called low C1 (LowC1, "LOH_C1"): 1 (33.33%) of 3
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean abCall lohCall
  1         2111        2111 0.5237 0.3300521 1.055848  FALSE    TRUE
  2          774         774 0.1542 0.8755722 1.194828   TRUE   FALSE
  3         1311        1311 0.2512 0.9862070 1.647893   TRUE   FALSE
  > plotTracks(fit)
  > 
  > proc.time()
     user  system elapsed 
     3.31    0.40    4.15 
Running the tests in 'tests/segmentByPairedPSCBS,calls.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Load SNP microarray data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data <- PSCBS::exampleData("paired.chr01")
  > str(data)
  'data.frame':	73346 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2224111 2319424 2543484 2926730 2941694 3084986 3155127 3292731 3695086 ...
   $ CT        : num  1.625 1.071 1.406 1.18 0.856 ...
   $ betaT     : num  0.757 0.771 0.834 0.778 0.229 ...
   $ CN        : num  2.36 2.13 2.59 1.93 1.71 ...
   $ betaN     : num  0.827 0.875 0.887 0.884 0.103 ...
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Paired PSCBS segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Drop single-locus outliers
  > dataS <- dropSegmentationOutliers(data)
  > 
  > # Find centromere
  > gaps <- findLargeGaps(dataS, minLength=2e6)
  > knownSegments <- gapsToSegments(gaps)
  > 
  > 
  > # Run light-weight tests by default
  > if (Sys.getenv("_R_CHECK_FULL_") == "") {
  +   # Use only every 5th data point
  +   dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  +   # Number of segments (for assertion)
  +   nSegs <- 4L
  +   # Number of bootstrap samples (see below)
  +   B <- 100L
  + } else {
  +   # Full tests
  +   nSegs <- 11L
  +   B <- 1000L
  + }
  > 
  > str(dataS)
  'data.frame':	14670 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2941694 3710825 4240737 4276892 4464544 4714611 5095111 5034491 5158416 ...
   $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
   $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
   $ CN        : num  2.36 2.13 2.26 2.01 2.32 ...
   $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
  > 
  > # Paired PSCBS segmentation
  > fit <- segmentByPairedPSCBS(dataS, knownSegments=knownSegments,
  +                             seed=0xBEEF, verbose=-10)
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Calling genotypes from normal allele B fractions...
     num [1:14670] 0.8274 0.5072 0.1671 0.1609 0.0421 ...
    Called genotypes:
     num [1:14670] 1 0.5 0 0 0 0 1 0 1 0.5 ...
     - attr(*, "modelFit")=List of 1
      ..$ :List of 7
      .. ..$ flavor             : chr "density"
      .. ..$ cn                 : int 2
      .. ..$ nbrOfGenotypeGroups: int 3
      .. ..$ tau                : num [1:2] 0.315 0.677
      .. ..$ n                  : int 14640
      .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.551
      ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
    muN
       0  0.5    1 
    5221 4198 5251 
   Calling genotypes from normal allele B fractions...done
   Normalizing betaT using betaN (TumorBoost)...
    Normalized BAFs:
     num [1:14670] 0.9301 0.1667 0.0685 0.0995 0.0155 ...
     - attr(*, "modelFit")=List of 5
      ..$ method       : chr "normalizeTumorBoost"
      ..$ flavor       : chr "v4"
      ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. ..- attr(*, "modelFit")=List of 1
      .. .. ..$ :List of 7
      .. .. .. ..$ flavor             : chr "density"
      .. .. .. ..$ cn                 : int 2
      .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. ..$ n                  : int 14640
      .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      ..$ preserveScale: logi FALSE
      ..$ scaleFactor  : num NA
   Normalizing betaT using betaN (TumorBoost)...done
   Setup up data...
    'data.frame':	14670 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.9301 0.1667 0.0685 0.0995 0.0155 ...
      ..- attr(*, "modelFit")=List of 5
      .. ..$ method       : chr "normalizeTumorBoost"
      .. ..$ flavor       : chr "v4"
      .. ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. .. ..- attr(*, "modelFit")=List of 1
      .. .. .. ..$ :List of 7
      .. .. .. .. ..$ flavor             : chr "density"
      .. .. .. .. ..$ cn                 : int 2
      .. .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      .. ..$ preserveScale: logi FALSE
      .. ..$ scaleFactor  : num NA
     $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 12
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	14658 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Keeping only current chromosome for 'knownSegments'...
    Chromosome: 1
    Known segments for this chromosome:
      chromosome     start       end   length
    1          1      -Inf 120992603      Inf
    2          1 120992604 141510002 20517398
    3          1 141510003       Inf      Inf
   Keeping only current chromosome for 'knownSegments'...done
   alphaTCN: 0.009
   alphaDH: 0.001
   Number of loci: 14658
   Calculating DHs...
    Number of SNPs: 14658
    Number of heterozygous SNPs: 4196 (28.63%)
    Normalized DHs:
     num [1:14658] NA NA NA NA NA ...
   Calculating DHs...done
   Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
   Produced 2 seeds from this stream for future usage
   Identification of change points by total copy numbers...
    Segmenting by CBS...
     Chromosome: 1
     Segmenting multiple segments on current chromosome...
      Number of segments: 3
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
      Produced 3 seeds from this stream for future usage
       Segmenting by CBS...
        Chromosome: 1
         Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       Segmenting by CBS...
        Chromosome: 1
         Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
     Segmenting multiple segments on current chromosome...done
    Segmenting by CBS...done
    List of 4
     $ data   :'data.frame':	14658 obs. of  4 variables:
      ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
      ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
      ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
      ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
     $ output :'data.frame':	4 obs. of  6 variables:
      ..$ sampleName: chr [1:4] NA NA NA NA
      ..$ chromosome: int [1:4] 1 1 1 1
      ..$ start     : num [1:4] 5.54e+05 1.21e+08 1.42e+08 1.85e+08
      ..$ end       : num [1:4] 1.21e+08 1.42e+08 1.85e+08 2.47e+08
      ..$ nbrOfLoci : int [1:4] 7586 0 2681 4391
      ..$ mean      : num [1:4] 1.39 NA 2.07 2.63
     $ segRows:'data.frame':	4 obs. of  2 variables:
      ..$ startRow: int [1:4] 1 NA 7587 10268
      ..$ endRow  : int [1:4] 7586 NA 10267 14658
     $ params :List of 5
      ..$ alpha        : num 0.009
      ..$ undo         : num 0
      ..$ joinSegments : logi TRUE
      ..$ knownSegments:'data.frame':	4 obs. of  3 variables:
      .. ..$ chromosome: int [1:4] 1 1 2 1
      .. ..$ start     : num [1:4] -Inf -Inf -Inf 1.42e+08
      .. ..$ end       : num [1:4] 1.21e+08 Inf Inf Inf
      ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
     - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
     - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.27 0 0.26 NA NA
      ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
     - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
     - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
   Identification of change points by total copy numbers...done
   Restructure TCN segmentation results...
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
    1          1    554484 120992603         7586  1.3853
    2          1 120992604 141510002            0      NA
    3          1 141510003 185449813         2681  2.0689
    4          1 185449813 247137334         4391  2.6341
    Number of TCN segments: 4
   Restructure TCN segmentation results...done
   Total CN segment #1 ([    554484,1.20993e+08]) of 4...
    Number of TCN loci in segment: 7586
    Locus data for TCN segment:
    'data.frame':	7586 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
     $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     $ rho       : num  NA NA NA NA NA ...
    Number of loci: 7586
    Number of SNPs: 2108 (27.79%)
    Number of heterozygous SNPs: 2108 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	7586 obs. of  4 variables:
       ..$ chromosome: int [1:7586] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:7586] 554484 730720 782343 878522 916294 ...
       ..$ y         : num [1:7586] NA NA NA NA NA ...
       ..$ index     : int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 554484
       ..$ end       : num 1.21e+08
       ..$ nbrOfLoci : int 2108
       ..$ mean      : num 0.512
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 10
       ..$ endRow  : int 7574
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 554484
       .. ..$ end       : num 1.21e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.12 0 0.13 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       10   7574
      int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
     DH segmentation rows:
       startRow endRow
     1       10   7574
    Segmenting DH signals...done
    DH segmentation table:
      dhStart     dhEnd dhNbrOfLoci dhMean
    1  554484 120992603        2108 0.5116
      startRow endRow
    1       10   7574
    Rows:
    [1] 1
    TCN segmentation rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
      startRow endRow
    1       10   7574
    NULL
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
      startRow endRow
    1        1   7586
    Total CN segmentation table (expanded):
      chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    1          1   554484 120992603         7586  1.3853         2108         2108
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1     1    1          1   554484 120992603         7586  1.3853         2108
      tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
    1         2108  554484 120992603        2108 0.5116
   Total CN segment #1 ([    554484,1.20993e+08]) of 4...done
   Total CN segment #2 ([1.20993e+08,1.4151e+08]) of 4...
    Number of TCN loci in segment: 0
    Locus data for TCN segment:
    'data.frame':	0 obs. of  9 variables:
     $ chromosome: int 
     $ x         : num 
     $ CT        : num 
     $ betaT     : num 
     $ betaTN    : num 
     $ betaN     : num 
     $ muN       : num 
     $ index     : int 
     $ rho       : num 
    Number of loci: 0
    Number of SNPs: 0 (NaN%)
    Number of heterozygous SNPs: 0 (NaN%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: NA
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	0 obs. of  4 variables:
       ..$ chromosome: int(0) 
       ..$ x         : num(0) 
       ..$ y         : num(0) 
       ..$ index     : int(0) 
      $ output :'data.frame':	0 obs. of  6 variables:
       ..$ sampleName: chr(0) 
       ..$ chromosome: num(0) 
       ..$ start     : num(0) 
       ..$ end       : num(0) 
       ..$ nbrOfLoci : int(0) 
       ..$ mean      : num(0) 
      $ segRows:'data.frame':	0 obs. of  2 variables:
       ..$ startRow: int(0) 
       ..$ endRow  : int(0) 
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	0 obs. of  3 variables:
       .. ..$ chromosome: int(0) 
       .. ..$ start     : num(0) 
       .. ..$ end       : num(0) 
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.02 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
      int(0) 
     DH segmentation rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
    Segmenting DH signals...done
    DH segmentation table:
       dhStart dhEnd dhNbrOfLoci dhMean
    NA      NA    NA          NA     NA
       startRow endRow
    NA       NA     NA
    Rows:
    [1] 2
    TCN segmentation rows:
      startRow endRow
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    2       NA     NA
       startRow endRow
    NA       NA     NA
      startRow endRow
    1        1   7586
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
      startRow endRow
    1        1   7586
    2       NA     NA
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    2          1 120992604 141510002            0      NA            0            0
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    2     2    1          1 120992604 141510002            0      NA            0
      tcnNbrOfHets dhStart dhEnd dhNbrOfLoci dhMean
    2            0      NA    NA          NA     NA
   Total CN segment #2 ([1.20993e+08,1.4151e+08]) of 4...done
   Total CN segment #3 ([1.4151e+08,1.8545e+08]) of 4...
    Number of TCN loci in segment: 2681
    Locus data for TCN segment:
    'data.frame':	2681 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
     $ CT        : num  2.27 1.55 1.47 1.5 1.81 ...
     $ betaT     : num  0.34 0.55 0.048 0.813 0.575 ...
     $ betaTN    : num  0.441 0.629 -0.05 0.958 0.872 ...
     $ betaN     : num  0.3851 0.3933 0.0981 0.8552 0.7028 ...
     $ muN       : num  0.5 0.5 0 1 1 1 1 0.5 1 1 ...
     $ index     : int  7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     $ rho       : num  0.117 0.258 NA NA NA ...
    Number of loci: 2681
    Number of SNPs: 777 (28.98%)
    Number of heterozygous SNPs: 777 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	2681 obs. of  4 variables:
       ..$ chromosome: int [1:2681] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:2681] 1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
       ..$ y         : num [1:2681] 0.117 0.258 NA NA NA ...
       ..$ index     : int [1:2681] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.42e+08
       ..$ end       : num 1.85e+08
       ..$ nbrOfLoci : int 777
       ..$ mean      : num 0.0973
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 1
       ..$ endRow  : int 2677
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.42e+08
       .. ..$ end       : num 1.85e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.01 0 0.01 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        1   2677
      int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     DH segmentation rows:
       startRow endRow
     1     7587  10263
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 141510003 185449813         777 0.0973
      startRow endRow
    1     7587  10263
    Rows:
    [1] 3
    TCN segmentation rows:
      startRow endRow
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    3     7587  10267
      startRow endRow
    1     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    3          1 141510003 185449813         2681  2.0689          777          777
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    3     3    1          1 141510003 185449813         2681  2.0689          777
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    3          777 141510003 185449813         777 0.0973
   Total CN segment #3 ([1.4151e+08,1.8545e+08]) of 4...done
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...
    Number of TCN loci in segment: 4391
    Locus data for TCN segment:
    'data.frame':	4391 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
     $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
     $ betaT     : num  0.0811 0.5154 0.9473 0.3734 0.7506 ...
     $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
     $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
     $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
     $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     $ rho       : num  NA 0.2186 NA 0.0503 NA ...
    Number of loci: 4391
    Number of SNPs: 1311 (29.86%)
    Number of heterozygous SNPs: 1311 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	4391 obs. of  4 variables:
       ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
       ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
       ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.85e+08
       ..$ end       : num 2.47e+08
       ..$ nbrOfLoci : int 1311
       ..$ mean      : num 0.23
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 2
       ..$ endRow  : int 4388
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.85e+08
       .. ..$ end       : num 2.47e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.05 0 0.05 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        2   4388
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     DH segmentation rows:
       startRow endRow
     1    10269  14655
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 185449813 247137334        1311 0.2295
      startRow endRow
    1    10269  14655
    Rows:
    [1] 4
    TCN segmentation rows:
      startRow endRow
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    4    10268  14658
      startRow endRow
    1    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
    4    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    4          1 185449813 247137334         4391  2.6341         1311         1311
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    4     4    1          1 185449813 247137334         4391  2.6341         1311
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    4         1311 185449813 247137334        1311 0.2295
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...done
     chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
   1          1     1    1    554484 120992603         7586  1.3853         2108
   2          1     2    1 120992604 141510002            0      NA            0
   3          1     3    1 141510003 185449813         2681  2.0689          777
   4          1     4    1 185449813 247137334         4391  2.6341         1311
     tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
   1         2108    554484 120992603        2108 0.5116
   2            0        NA        NA          NA     NA
   3          777 141510003 185449813         777 0.0973
   4         1311 185449813 247137334        1311 0.2295
   Calculating (C1,C2) per segment...
   Calculating (C1,C2) per segment...done
   Number of segments: 4
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Post-segmenting TCNs...
   Number of segments: 4
   Number of chromosomes: 1
   [1] 1
   Chromosome 1 ('chr01') of 1...
    Rows:
    [1] 1 2 3 4
    Number of segments: 4
    TCN segment #1 ('1') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #1 ('1') of 4...done
    TCN segment #2 ('2') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #2 ('2') of 4...done
    TCN segment #3 ('3') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #3 ('3') of 4...done
    TCN segment #4 ('4') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #4 ('4') of 4...done
   Chromosome 1 ('chr01') of 1...done
   Update (C1,C2) per segment...
   Update (C1,C2) per segment...done
  Post-segmenting TCNs...done
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120992603        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 141510003 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120992603        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 141510003 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108        2108 0.5116 0.3382903 1.047010
  2            0          NA     NA        NA       NA
  3          777         777 0.0973 0.9337980 1.135102
  4         1311        1311 0.2295 1.0147870 1.619313
  > 
  > # Plot results
  > plotTracks(fit)
  > 
  > # Sanity check
  > stopifnot(nbrOfSegments(fit) == nSegs)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Bootstrap segment level estimates
  > # (used by the AB caller, which, if skipped here,
  > #  will do it automatically)
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- bootstrapTCNandDHByRegion(fit, B=B, verbose=-10)
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...
   Already done?
    tcn_2.5%    tcn_5%   tcn_95% tcn_97.5%   dh_2.5%     dh_5%    dh_95%  dh_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
     c1_2.5%     c1_5%    c1_95%  c1_97.5%   c2_2.5%     c2_5%    c2_95%  c2_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...
     Number of loci: 14658
     Number of SNPs: 4196
     Number of non-SNPs: 10462
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...done
     num [1:4, 1:100, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Segment #1 (chr 1, tcnId=1, dhId=1) of 4...
       chromosome tcnId dhId tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1   554484 120992603         7586  1.3853         2108
       tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean  c2Mean
     1         2108  554484 120992603        2108 0.5116 0.3382903 1.04701
     Number of TCNs: 7586
     Number of DHs: 2108
      int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:2108] 10 12 24 28 31 33 34 39 46 48 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:2108] 10 12 24 28 31 33 34 39 46 48 ...
      Non-polymorphic loci:
       int [1:5478] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs to resample for TCN:
       int [1:2108] 10 12 24 28 31 33 34 39 46 48 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:5478] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (2108,0,5478)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #1 (chr 1, tcnId=1, dhId=1) of 4...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 4...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     2          1     2    1 120992604 141510002            0      NA            0
       tcnNbrOfHets dhStart dhEnd dhNbrOfLoci dhMean c1Mean c2Mean
     2            0      NA    NA           0     NA     NA     NA
     Number of TCNs: 0
     Number of DHs: 0
      int 0
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int(0) 
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int 0
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int(0) 
      Non-polymorphic loci:
       int(0) 
      Heterozygous SNPs to resample for TCN:
       int(0) 
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int(0) 
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int(0) 
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (0,0,0)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 4...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 4...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     3          1     3    1 141510003 185449813         2681  2.0689          777
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean   c1Mean   c2Mean
     3          777 141510003 185449813         777 0.0973 0.933798 1.135102
     Number of TCNs: 2681
     Number of DHs: 777
      int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:777] 7587 7588 7594 7614 7616 7626 7627 7628 7635 7638 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:777] 7587 7588 7594 7614 7616 7626 7627 7628 7635 7638 ...
      Non-polymorphic loci:
       int [1:1904] 7589 7590 7591 7592 7593 7595 7596 7597 7598 7599 ...
      Heterozygous SNPs to resample for TCN:
       int [1:777] 7587 7588 7594 7614 7616 7626 7627 7628 7635 7638 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:1904] 7589 7590 7591 7592 7593 7595 7596 7597 7598 7599 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (777,0,1904)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 4...done
    Segment #4 (chr 1, tcnId=4, dhId=1) of 4...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     4          1     4    1 185449813 247137334         4391  2.6341         1311
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean   c1Mean   c2Mean
     4         1311 185449813 247137334        1311 0.2295 1.014787 1.619313
     Number of TCNs: 4391
     Number of DHs: 1311
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Non-polymorphic loci:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs to resample for TCN:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (1311,0,3080)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #4 (chr 1, tcnId=4, dhId=1) of 4...done
    Bootstrapped segment mean levels
     num [1:4, 1:100, 1:4] 1.39 NA 2.08 2.63 1.38 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...
      num [1:4, 1:100, 1:4] 1.39 NA 2.08 2.63 1.38 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...done
    Calculating polar (alpha,radius,manhattan) for change points...
      num [1:3, 1:100, 1:2] NA NA -0.0752 NA NA ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:2] "c1" "c2"
     Bootstrapped change points
      num [1:3, 1:100, 1:5] NA NA -1.73 NA NA ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Calculating polar (alpha,radius,manhattan) for change points...done
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...
     num [1:4, 1:4, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Field #1 ('tcn') of 4...
     Segment #1 of 4...
     Segment #1 of 4...done
     Segment #2 of 4...
     Segment #2 of 4...done
     Segment #3 of 4...
     Segment #3 of 4...done
     Segment #4 of 4...
     Segment #4 of 4...done
    Field #1 ('tcn') of 4...done
    Field #2 ('dh') of 4...
     Segment #1 of 4...
     Segment #1 of 4...done
     Segment #2 of 4...
     Segment #2 of 4...done
     Segment #3 of 4...
     Segment #3 of 4...done
     Segment #4 of 4...
     Segment #4 of 4...done
    Field #2 ('dh') of 4...done
    Field #3 ('c1') of 4...
     Segment #1 of 4...
     Segment #1 of 4...done
     Segment #2 of 4...
     Segment #2 of 4...done
     Segment #3 of 4...
     Segment #3 of 4...done
     Segment #4 of 4...
     Segment #4 of 4...done
    Field #3 ('c1') of 4...done
    Field #4 ('c2') of 4...
     Segment #1 of 4...
     Segment #1 of 4...done
     Segment #2 of 4...
     Segment #2 of 4...done
     Segment #3 of 4...
     Segment #3 of 4...done
     Segment #4 of 4...
     Segment #4 of 4...done
    Field #4 ('c2') of 4...done
    Bootstrap statistics
     num [1:4, 1:4, 1:4] 1.38 NA 2.06 2.63 1.38 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Statistical sanity checks (iff B >= 100)...
     Available summaries: 2.5%, 5%, 95%, 97.5%
     Available quantiles: 0.025, 0.05, 0.95, 0.975
      num [1:4, 1:4, 1:4] 1.38 NA 2.06 2.63 1.38 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
     Field #1 ('tcn') of 4...
      Seg   1. mean=1.3853, range=[1.37909,1.39287], n=7586
      Seg   2. mean=NA, range=[NA,NA], n=0
      Seg   3. mean=2.0689, range=[2.05903,2.079], n=2681
      Seg   4. mean=2.6341, range=[2.62504,2.64649], n=4391
     Field #1 ('tcn') of 4...done
     Field #2 ('dh') of 4...
      Seg   1. mean=0.5116, range=[0.502148,0.519941], n=2108
      Seg   2. mean=NA, range=[NA,NA], n=NA
      Seg   3. mean=0.0973, range=[0.0906366,0.105818], n=777
      Seg   4. mean=0.2295, range=[0.222919,0.237005], n=1311
     Field #2 ('dh') of 4...done
     Field #3 ('c1') of 4...
      Seg   1. mean=0.33829, range=[0.332209,0.345936], n=2108
      Seg   2. mean=NA, range=[NA,NA], n=NA
      Seg   3. mean=0.933798, range=[0.924112,0.941776], n=777
      Seg   4. mean=1.01479, range=[1.00381,1.02461], n=1311
     Field #3 ('c1') of 4...done
     Field #4 ('c2') of 4...
      Seg   1. mean=1.04701, range=[1.03882,1.05318], n=2108
      Seg   2. mean=NA, range=[NA,NA], n=NA
      Seg   3. mean=1.1351, range=[1.12454,1.1465], n=777
      Seg   4. mean=1.61931, range=[1.60862,1.63328], n=1311
     Field #4 ('c2') of 4...done
    Statistical sanity checks (iff B >= 100)...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...done
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...
     num [1:3, 1:4, 1:5] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Field #1 ('alpha') of 5...
     Changepoint #1 of 3...
     Changepoint #1 of 3...done
     Changepoint #2 of 3...
     Changepoint #2 of 3...done
     Changepoint #3 of 3...
     Changepoint #3 of 3...done
    Field #1 ('alpha') of 5...done
    Field #2 ('radius') of 5...
     Changepoint #1 of 3...
     Changepoint #1 of 3...done
     Changepoint #2 of 3...
     Changepoint #2 of 3...done
     Changepoint #3 of 3...
     Changepoint #3 of 3...done
    Field #2 ('radius') of 5...done
    Field #3 ('manhattan') of 5...
     Changepoint #1 of 3...
     Changepoint #1 of 3...done
     Changepoint #2 of 3...
     Changepoint #2 of 3...done
     Changepoint #3 of 3...
     Changepoint #3 of 3...done
    Field #3 ('manhattan') of 5...done
    Field #4 ('d1') of 5...
     Changepoint #1 of 3...
     Changepoint #1 of 3...done
     Changepoint #2 of 3...
     Changepoint #2 of 3...done
     Changepoint #3 of 3...
     Changepoint #3 of 3...done
    Field #4 ('d1') of 5...done
    Field #5 ('d2') of 5...
     Changepoint #1 of 3...
     Changepoint #1 of 3...done
     Changepoint #2 of 3...
     Changepoint #2 of 3...done
     Changepoint #3 of 3...
     Changepoint #3 of 3...done
    Field #5 ('d2') of 5...done
    Bootstrap statistics
     num [1:3, 1:4, 1:5] NA NA -1.77 NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...done
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108        2108 0.5116 0.3382903 1.047010
  2            0          NA     NA        NA       NA
  3          777         777 0.0973 0.9337980 1.135102
  4         1311        1311 0.2295 1.0147870 1.619313
  > plotTracks(fit)
  > 
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments with run of homozygosity (ROH)
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- callROH(fit, verbose=-10)
  Calling ROH...
   Segment #1 of 4...
    Calling ROH for a single segment...
     Number of SNPs: 7586
    Calling ROH for a single segment...done
   Segment #1 of 4...done
   Segment #2 of 4...
    Calling ROH for a single segment...
     Number of SNPs: 0
    Calling ROH for a single segment...done
   Segment #2 of 4...done
   Segment #3 of 4...
    Calling ROH for a single segment...
     Number of SNPs: 2681
    Calling ROH for a single segment...done
   Segment #3 of 4...done
   Segment #4 of 4...
    Calling ROH for a single segment...
     Number of SNPs: 4391
    Calling ROH for a single segment...done
   Segment #4 of 4...done
   ROH calls:
    logi [1:4] FALSE NA FALSE FALSE
      Mode   FALSE    NA's 
   logical       3       1 
  Calling ROH...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean rohCall
  1         2108        2108 0.5116 0.3382903 1.047010   FALSE
  2            0          NA     NA        NA       NA      NA
  3          777         777 0.0973 0.9337980 1.135102   FALSE
  4         1311        1311 0.2295 1.0147870 1.619313   FALSE
  > plotTracks(fit)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Estimate background
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > kappa <- estimateKappa(fit, verbose=-10)
  Estimate global background (including normal contamination and more)...
   Number of segments: 3
   Estimating threshold Delta0.5 from the empirical density of C1:s...
    adjust: 1
    minDensity: 0.2
    ploidy: 2
    All peaks:
      type         x  density
    1 peak 0.3362194 1.101242
    3 peak 0.9811492 1.065635
    C1=0 and C1=1 peaks:
      type         x  density
    1 peak 0.3362194 1.101242
    3 peak 0.9811492 1.065635
    Estimate of Delta0.5: 0.65868427808456
   Estimating threshold Delta0.5 from the empirical density of C1:s...done
   Number of segments with C1 < Delta0.5: 1
   Estimate of kappa: 0.33829026
  Estimate global background (including normal contamination and more)...done
  Warning message:
  In density.default(c1, weights = weights, adjust = adjust, from = from,  :
    Selecting bandwidth *not* using 'weights'
  > print(kappa)
  [1] 0.3382903
  > ## [1] 0.226011
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments in allelic balance (AB)
  > # NOTE: Ideally, this should be done on whole-genome data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Explicitly estimate the threshold in DH for calling AB
  > # (which be done by default by the caller, if skipped here)
  > deltaAB <- estimateDeltaAB(fit, flavor="qq(DH)", verbose=-10)
  Estimating DH threshold for calling allelic imbalances...
   flavor: qq(DH)
   scale: 1
   Estimating DH threshold for AB caller...
    quantile #1: 0.05
    Symmetric quantile #2: 0.9
    Number of segments: 3
    Weighted 5% quantile of DH: 0.257710
    Number of segments with small DH: 2
    Number of data points: 7072
    Number of finite data points: 2088
    Estimate of (1-0.9):th and 50% quantiles: (0.0310411,0.163658)
    Estimate of 0.9:th "symmetric" quantile: 0.296275
   Estimating DH threshold for AB caller...done
   Estimated delta: 0.296
  Estimating DH threshold for calling allelic imbalances...done
  > if (Sys.getenv("_R_CHECK_FULL_") == "") {
  +   # Ad hoc workaround for not utilizing all of the data
  +   # in the test, which results in a poor estimate
  +   deltaAB <- 0.165
  + }
  > print(deltaAB)
  [1] 0.165
  > ## [1] 0.1657131
  > 
  > fit <- callAB(fit, delta=deltaAB, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in DH): 0.165
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called allelic balance (AB): 1 (25.00%) of 4
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean rohCall abCall
  1         2108        2108 0.5116 0.3382903 1.047010   FALSE  FALSE
  2            0          NA     NA        NA       NA      NA     NA
  3          777         777 0.0973 0.9337980 1.135102   FALSE   TRUE
  4         1311        1311 0.2295 1.0147870 1.619313   FALSE  FALSE
  > plotTracks(fit)
  > 
  > # Even if not explicitly specified, the estimated
  > # threshold parameter is returned by the caller
  > stopifnot(fit$params$deltaAB == deltaAB)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments in loss-of-heterozygosity (LOH)
  > # NOTE: Ideally, this should be done on whole-genome data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Explicitly estimate the threshold in C1 for calling LOH
  > # (which be done by default by the caller, if skipped here)
  > deltaLOH <- estimateDeltaLOH(fit, flavor="minC1|nonAB", verbose=-10)
  Estimating DH threshold for calling LOH...
   flavor: minC1|nonAB
   Estimating DH threshold for calling LOH as the midpoint between guessed C1=0 and C1=1...
    Argument 'midpoint': 0.5
    Number of segments: 4
    Number of segments in allelic balance: 1 (25.0%) of 4
    Number of segments not in allelic balance: 2 (50.0%) of 4
    Number of segments in allelic balance and TCN <= 3.00: 1 (25.0%) of 4
    C: 2.07
    Corrected C1 (=C/2): 1.03
    Number of DHs: 777
    Weights: 1
    Weighted median of (corrected) C1 in allelic balance: 1.034
    Smallest C1 among segments not in allelic balance: 0.338
    There are 1 segments with in total 2108 heterozygous SNPs with this level.
    Midpoint between the two: 0.686
   Estimating DH threshold for calling LOH as the midpoint between guessed C1=0 and C1=1...done
   delta: 0.686
  Estimating DH threshold for calling LOH...done
  > print(deltaLOH)
  [1] 0.6863701
  > ## [1] 0.625175
  > 
  > fit <- callLOH(fit, delta=deltaLOH, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in C1): 0.68637013
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called low C1 (LowC1, "LOH_C1"): 1 (25.00%) of 4
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean rohCall abCall lohCall
  1         2108        2108 0.5116 0.3382903 1.047010   FALSE  FALSE    TRUE
  2            0          NA     NA        NA       NA      NA     NA      NA
  3          777         777 0.0973 0.9337980 1.135102   FALSE   TRUE   FALSE
  4         1311        1311 0.2295 1.0147870 1.619313   FALSE  FALSE   FALSE
  > plotTracks(fit)
  > 
  > # Even if not explicitly specified, the estimated
  > # threshold parameter is returned by the caller
  > stopifnot(fit$params$deltaLOH == deltaLOH)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments that are gained, copy neutral, and lost
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- callGNL(fit, verbose=-10)
  Calling gain, neutral, and loss based TCNs of AB segments...
   Calling neutral TCNs...
    callCopyNeutralByTCNofAB...
     Alpha: 0.05
     Delta CN: 0.33085487
     Calling copy-neutral segments...
      Retrieve TCN confidence intervals for all segments...
       Interval: [0.025,0.975]
      Retrieve TCN confidence intervals for all segments...done
      Estimating TCN confidence interval of copy-neutral AB segments...
       calcStatsForCopyNeutralABs...
        Identifying copy neutral AB segments...
         Number of AB segments: 1
         Identifying segments that are copy neutral states...
          Number of segments in allelic balance: 1
         Identifying segments that are copy neutral states...done
         Number of copy-neutral AB segments: 1
         Extracting all copy neutral AB segments across all chromosomes into one big segment...
            chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
          3          1     3    1 141510003 185449813         2681  2.0689          777
            tcnNbrOfHets dhNbrOfLoci dhMean   c1Mean   c2Mean rohCall abCall lohCall
          3          777         777 0.0973 0.933798 1.135102   FALSE   TRUE   FALSE
         Extracting all copy neutral AB segments across all chromosomes into one big segment...done
        Identifying copy neutral AB segments...done
        Bootstrap the identified copy-neutral states...
        Bootstrap the identified copy-neutral states...done
          tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets dhNbrOfLoci dhMean   c1Mean
        3         2681  2.0689          777          777         777 0.0973 0.933798
            c2Mean tcn_2.5%   tcn_5%  tcn_95% tcn_97.5%    dh_2.5%      dh_5%    dh_95%
        3 1.135102 2.055164 2.057694 2.078831  2.081454 0.08974138 0.09080508 0.1035891
           dh_97.5%  c1_2.5%    c1_5%    c1_95%  c1_97.5%  c2_2.5%    c2_5%   c2_95%
        3 0.1050478 0.923788 0.925412 0.9417056 0.9433752 1.124908 1.126631 1.143571
          c2_97.5%
        3 1.145214
       calcStatsForCopyNeutralABs...done
       Bootstrap statistics for copy-neutral AB segments:
         tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets dhNbrOfLoci dhMean   c1Mean
       3         2681  2.0689          777          777         777 0.0973 0.933798
           c2Mean tcn_2.5%   tcn_5%  tcn_95% tcn_97.5%    dh_2.5%      dh_5%    dh_95%
       3 1.135102 2.055164 2.057694 2.078831  2.081454 0.08974138 0.09080508 0.1035891
          dh_97.5%  c1_2.5%    c1_5%    c1_95%  c1_97.5%  c2_2.5%    c2_5%   c2_95%
       3 0.1050478 0.923788 0.925412 0.9417056 0.9433752 1.124908 1.126631 1.143571
         c2_97.5%
       3 1.145214
       [1] "TCN statistics:"
         tcnMean  tcn_2.5%    tcn_5%   tcn_95% tcn_97.5% 
        2.068900  2.055164  2.057694  2.078831  2.081454 
       95%-confidence interval of TCN mean for the copy-neutral state: [2.05516,2.08145] (mean=2.0689)
      Estimating TCN confidence interval of copy-neutral AB segments...done
      Identify all copy-neutral segments...
       DeltaCN: +/-0.330855
       Call ("acceptance") region: [1.72431,2.41231]
       Total number of segments: 4
       Number of segments called allelic balance: 1
       Number of segments called copy neutral: 1
       Number of AB segments called copy neutral: 1
       Number of non-AB segments called copy neutral: 0
      Identify all copy-neutral segments...done
     Calling copy-neutral segments...done
    callCopyNeutralByTCNofAB...done
   Calling neutral TCNs...done
   Number of NTCN calls: 1 (25.00%) of 4
   Mean TCN of AB segments: 2.06831
   Calling loss...
    Number of loss calls: 1 (25.00%) of 4
   Calling loss...done
   Calling gain...
    Number of loss calls: 1 (25.00%) of 4
   Calling gain...done
  Calling gain, neutral, and loss based TCNs of AB segments...done
  Warning message:
  In density.default(c1, weights = weights, adjust = adjust, from = from,  :
    Selecting bandwidth *not* using 'weights'
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120992603         7586  1.3853         2108
  2          1     2    1 120992604 141510002            0      NA            0
  3          1     3    1 141510003 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean rohCall abCall lohCall
  1         2108        2108 0.5116 0.3382903 1.047010   FALSE  FALSE    TRUE
  2            0          NA     NA        NA       NA      NA     NA      NA
  3          777         777 0.0973 0.9337980 1.135102   FALSE   TRUE   FALSE
  4         1311        1311 0.2295 1.0147870 1.619313   FALSE  FALSE   FALSE
    ntcnCall lossCall gainCall
  1    FALSE     TRUE    FALSE
  2       NA       NA       NA
  3     TRUE    FALSE    FALSE
  4    FALSE    FALSE     TRUE
  > plotTracks(fit)
  > 
  > proc.time()
     user  system elapsed 
     5.35    0.48    6.51 
Running the tests in 'tests/segmentByPairedPSCBS,futures.R' failed.
Complete output:
  > library(PSCBS)
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > library(utils)
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Load SNP microarray data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data <- PSCBS::exampleData("paired.chr01")
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Paired PSCBS segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Drop single-locus outliers
  > dataS <- dropSegmentationOutliers(data)
  > 
  > # Run light-weight tests by default
  > if (Sys.getenv("_R_CHECK_FULL_") == "") {
  +   # Use only every 5th data point
  +   dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  +   # Number of segments (for assertion)
  +   nSegs <- 4L
  + } else {
  +   # Full tests
  +   nSegs <- 11L
  + }
  > 
  > str(dataS)
  'data.frame':	14670 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2941694 3710825 4240737 4276892 4464544 4714611 5095111 5034491 5158416 ...
   $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
   $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
   $ CN        : num  2.36 2.13 2.26 2.01 2.32 ...
   $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
  > 
  > 
  > ## Create multiple chromosomes
  > data <- list()
  > for (cc in 1:3) {
  +   dataS$chromosome <- cc
  +   data[[cc]] <- dataS
  + }
  > data <- Reduce(rbind, data)
  > str(data)
  'data.frame':	44010 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2941694 3710825 4240737 4276892 4464544 4714611 5095111 5034491 5158416 ...
   $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
   $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
   $ CN        : num  2.36 2.13 2.26 2.01 2.32 ...
   $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
  > 
  > 
  > message("*** segmentByPairedPSCBS() via futures ...")
  *** segmentByPairedPSCBS() via futures ...
  > 
  > library("future")
  > oplan <- plan()
  > 
  > strategies <- c("sequential", "multisession")
  > 
  > ## Test 'future.batchtools' futures?
  > pkg <- "future.batchtools"
  > if (require(pkg, character.only=TRUE)) {
  +   strategies <- c(strategies, "batchtools_local")
  + }
  Loading required package: future.batchtools
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'future.batchtools'
  > 
  > message("Future strategies to test: ", paste(sQuote(strategies), collapse=", "))
  Future strategies to test: 'sequential', 'multisession'
  > 
  > fits <- list()
  > for (strategy in strategies) {
  +   message(sprintf("- segmentByPairedPSCBS() using '%s' futures ...", strategy))
  +   plan(strategy)
  +   fit <- segmentByPairedPSCBS(data, seed=0xBEEF, verbose=TRUE)
  +   fits[[strategy]] <- fit
  +   equal <- all.equal(fit, fits[[1]])
  +   if (!equal) {
  +     str(fit)
  +     str(fits[[1]])
  +     print(equal)
  +     stop(sprintf("segmentByPairedPSCBS() using '%s' futures does not produce the same results as when using '%s' futures", strategy, names(fits)[1]))
  +   }
  + }
  - segmentByPairedPSCBS() using 'sequential' futures ...
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Calling genotypes from normal allele B fractions...
     num [1:44010] 0.8274 0.5072 0.1671 0.1609 0.0421 ...
    Called genotypes:
     num [1:44010] 1 0.5 0 0 0 0 1 0 1 0.5 ...
     - attr(*, "modelFit")=List of 1
      ..$ :List of 7
      .. ..$ flavor             : chr "density"
      .. ..$ cn                 : int 2
      .. ..$ nbrOfGenotypeGroups: int 3
      .. ..$ tau                : num [1:2] 0.312 0.678
      .. ..$ n                  : int 43920
      .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. ..$ density: num [1:2] 0.465 0.496
      ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
    muN
        0   0.5     1 
    15627 12633 15750 
   Calling genotypes from normal allele B fractions...done
   Normalizing betaT using betaN (TumorBoost)...
    Normalized BAFs:
     num [1:44010] 0.9301 0.1667 0.0685 0.0995 0.0155 ...
     - attr(*, "modelFit")=List of 5
      ..$ method       : chr "normalizeTumorBoost"
      ..$ flavor       : chr "v4"
      ..$ delta        : num [1:44010] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. ..- attr(*, "modelFit")=List of 1
      .. .. ..$ :List of 7
      .. .. .. ..$ flavor             : chr "density"
      .. .. .. ..$ cn                 : int 2
      .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. .. ..$ n                  : int 43920
      .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      ..$ preserveScale: logi FALSE
      ..$ scaleFactor  : num NA
   Normalizing betaT using betaN (TumorBoost)...done
   Setup up data...
    'data.frame':	44010 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.9301 0.1667 0.0685 0.0995 0.0155 ...
      ..- attr(*, "modelFit")=List of 5
      .. ..$ method       : chr "normalizeTumorBoost"
      .. ..$ flavor       : chr "v4"
      .. ..$ delta        : num [1:44010] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. .. ..- attr(*, "modelFit")=List of 1
      .. .. .. ..$ :List of 7
      .. .. .. .. ..$ flavor             : chr "density"
      .. .. .. .. ..$ cn                 : int 2
      .. .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. .. .. ..$ n                  : int 43920
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      .. ..$ preserveScale: logi FALSE
      .. ..$ scaleFactor  : num NA
     $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. ..$ n                  : int 43920
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 36
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	43974 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Segmenting multiple chromosomes...
    Number of chromosomes: 3
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
    Chromosome #1 ('Chr01') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
     Segmenting paired tumor-normal signals using Paired PSCBS...
      Setup up data...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Setup up data...done
      Ordering data along genome...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Ordering data along genome...done
      Keeping only current chromosome for 'knownSegments'...
       Chromosome: 1
       Known segments for this chromosome:
       [1] chromosome start      end       
       <0 rows> (or 0-length row.names)
      Keeping only current chromosome for 'knownSegments'...done
      alphaTCN: 0.009
      alphaDH: 0.001
      Number of loci: 14658
      Calculating DHs...
       Number of SNPs: 14658
       Number of heterozygous SNPs: 4209 (28.71%)
       Normalized DHs:
        num [1:14658] NA NA NA NA NA ...
      Calculating DHs...done
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
      Produced 2 seeds from this stream for future usage
      Identification of change points by total copy numbers...
       Segmenting by CBS...
        Chromosome: 1
         Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 1 1 1
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 1
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.67 0 0.67 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          1    554484 143926517         7599  1.3859
       2          1 143926517 185449813         2668  2.0704
       3          1 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
       Number of heterozygous SNPs: 2120 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.08 0 0.08 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          1   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          1   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
       Number of loci: 2668
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          1 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          1 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.05 0 0.05 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          1 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          1 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          1     1    1    554484 143926517         7599  1.3859         2120
      2          1     2    1 143926517 185449813         2668  2.0704          775
      3          1     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 1
      Chromosome 1 ('chr01') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr01') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    Chromosome #1 ('Chr01') of 3...done
    Chromosome #2 ('Chr02') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  14659 14660 14661 14662 14663 14664 14665 14666 14667 14668 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
     Segmenting paired tumor-normal signals using Paired PSCBS...
      Setup up data...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Setup up data...done
      Ordering data along genome...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Ordering data along genome...done
      Keeping only current chromosome for 'knownSegments'...
       Chromosome: 2
       Known segments for this chromosome:
       [1] chromosome start      end       
       <0 rows> (or 0-length row.names)
      Keeping only current chromosome for 'knownSegments'...done
      alphaTCN: 0.009
      alphaDH: 0.001
      Number of loci: 14658
      Calculating DHs...
       Number of SNPs: 14658
       Number of heterozygous SNPs: 4209 (28.71%)
       Normalized DHs:
        num [1:14658] NA NA NA NA NA ...
      Calculating DHs...done
      Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
      Produced 2 seeds from this stream for future usage
      Identification of change points by total copy numbers...
       Segmenting by CBS...
        Chromosome: 2
         Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 2 2 2 2 2 2 2 2 2 2 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 2 2 2
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 2
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.65 0 0.66 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          2    554484 143926517         7599  1.3859
       2          2 143926517 185449813         2668  2.0704
       3          2 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
       Number of heterozygous SNPs: 2120 (100.00%)
       Chromosome: 2
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 2
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.06 0 0.06 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          2   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          2   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
       Number of loci: 2668
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 2
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 2
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.01 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          2 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          2 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 2
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 2
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          2 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          2 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          2     1    1    554484 143926517         7599  1.3859         2120
      2          2     2    1 143926517 185449813         2668  2.0704          775
      3          2     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 2
      Chromosome 1 ('chr02') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr02') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    Chromosome #2 ('Chr02') of 3...done
    Chromosome #3 ('Chr03') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  29317 29318 29319 29320 29321 29322 29323 29324 29325 29326 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
     Segmenting paired tumor-normal signals using Paired PSCBS...
      Setup up data...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Setup up data...done
      Ordering data along genome...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Ordering data along genome...done
      Keeping only current chromosome for 'knownSegments'...
       Chromosome: 3
       Known segments for this chromosome:
       [1] chromosome start      end       
       <0 rows> (or 0-length row.names)
      Keeping only current chromosome for 'knownSegments'...done
      alphaTCN: 0.009
      alphaDH: 0.001
      Number of loci: 14658
      Calculating DHs...
       Number of SNPs: 14658
       Number of heterozygous SNPs: 4209 (28.71%)
       Normalized DHs:
        num [1:14658] NA NA NA NA NA ...
      Calculating DHs...done
      Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
      Produced 2 seeds from this stream for future usage
      Identification of change points by total copy numbers...
       Segmenting by CBS...
        Chromosome: 3
         Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 3 3 3 3 3 3 3 3 3 3 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 3 3 3
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 3
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.69 0.01 0.7 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          3    554484 143926517         7599  1.3859
       2          3 143926517 185449813         2668  2.0704
       3          3 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
       Number of heterozygous SNPs: 2120 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.05 0 0.04 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          3   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          3   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
       Number of loci: 2668
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          3 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          3 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.04 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          3 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          3 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          3     1    1    554484 143926517         7599  1.3859         2120
      2          3     2    1 143926517 185449813         2668  2.0704          775
      3          3     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 3
      Chromosome 1 ('chr03') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr03') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    Chromosome #3 ('Chr03') of 3...done
    Merging (independently) segmented chromosome...
     List of 5
      $ data      :Classes 'PairedPSCNData' and 'data.frame':	43974 obs. of  8 variables:
       ..$ chromosome: int [1:43974] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:43974] 554484 730720 782343 878522 916294 ...
       ..$ CT        : num [1:43974] 1.88 1.8 1.59 1.64 1.53 ...
       ..$ betaT     : num [1:43974] 0.0646 0.1672 0.9284 0.113 0.7209 ...
       ..$ betaTN    : num [1:43974] -0.0515 -0.1172 1.0194 0.031 0.8604 ...
       ..$ betaN     : num [1:43974] 0.116 0.284 0.909 0.082 0.86 ...
       ..$ muN       : num [1:43974] 0 0 1 0 1 1 1 0 1 0.5 ...
       ..$ rho       : num [1:43974] NA NA NA NA NA ...
      $ output    :Classes 'PairedPSCNSegments' and 'data.frame':	11 obs. of  15 variables:
       ..$ chromosome  : int [1:11] 1 1 1 NA 2 2 2 NA 3 3 ...
       ..$ tcnId       : int [1:11] 1 2 3 NA 1 2 3 NA 1 2 ...
       ..$ dhId        : int [1:11] 1 1 1 NA 1 1 1 NA 1 1 ...
       ..$ tcnStart    : num [1:11] 5.54e+05 1.44e+08 1.85e+08 NA 5.54e+05 ...
       ..$ tcnEnd      : num [1:11] 1.44e+08 1.85e+08 2.47e+08 NA 1.44e+08 ...
       ..$ tcnNbrOfLoci: int [1:11] 7599 2668 4391 NA 7599 2668 4391 NA 7599 2668 ...
       ..$ tcnMean     : num [1:11] 1.39 2.07 2.63 NA 1.39 ...
       ..$ tcnNbrOfSNPs: int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ tcnNbrOfHets: int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ dhStart     : num [1:11] 5.54e+05 1.44e+08 1.85e+08 NA 5.54e+05 ...
       ..$ dhEnd       : num [1:11] 1.44e+08 1.85e+08 2.47e+08 NA 1.44e+08 ...
       ..$ dhNbrOfLoci : int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ dhMean      : num [1:11] 0.51 0.097 0.23 NA 0.51 ...
       ..$ c1Mean      : num [1:11] 0.339 0.935 1.015 NA 0.339 ...
       ..$ c2Mean      : num [1:11] 1.05 1.14 1.62 NA 1.05 ...
      $ tcnSegRows:'data.frame':	11 obs. of  2 variables:
       ..$ startRow: int [1:11] 1 7600 10268 NA 14659 22258 24926 NA 29317 36916 ...
       ..$ endRow  : int [1:11] 7599 10267 14658 NA 22257 24925 29316 NA 36915 39583 ...
      $ dhSegRows :'data.frame':	11 obs. of  2 variables:
       ..$ startRow: int [1:11] 10 7614 10269 NA 14668 22272 24927 NA 29326 36930 ...
       ..$ endRow  : int [1:11] 7594 10263 14655 NA 22252 24921 29313 NA 36910 39579 ...
      $ params    :List of 7
       ..$ alphaTCN     : num 0.009
       ..$ alphaDH      : num 0.001
       ..$ flavor       : chr "tcn&dh"
       ..$ tbn          : logi FALSE
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	0 obs. of  3 variables:
       .. ..$ chromosome: int(0) 
       .. ..$ start     : int(0) 
       .. ..$ end       : int(0) 
       ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
      - attr(*, "class")= chr [1:3] "PairedPSCBS" "PSCBS" "AbstractCBS"
    Merging (independently) segmented chromosome...done
      chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1          1     1    1    554484 143926517         7599  1.3859         2120
    2          1     2    1 143926517 185449813         2668  2.0704          775
    3          1     3    1 185449813 247137334         4391  2.6341         1314
    4         NA    NA   NA        NA        NA           NA      NA           NA
    5          2     1    1    554484 143926517         7599  1.3859         2120
    6          2     2    1 143926517 185449813         2668  2.0704          775
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
    1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
    3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    4           NA        NA        NA          NA     NA        NA       NA
    5         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    6          775 143926517 185449813         775 0.0970 0.9347856 1.135614
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    6           2     2    1 143926517 185449813         2668  2.0704          775
    7           2     3    1 185449813 247137334         4391  2.6341         1314
    8          NA    NA   NA        NA        NA           NA      NA           NA
    9           3     1    1    554484 143926517         7599  1.3859         2120
    10          3     2    1 143926517 185449813         2668  2.0704          775
    11          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
    6           775 143926517 185449813         775 0.0970 0.9347856 1.135614
    7          1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    8            NA        NA        NA          NA     NA        NA       NA
    9          2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    10          775 143926517 185449813         775 0.0970 0.9347856 1.135614
    11         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
   Segmenting multiple chromosomes...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  - segmentByPairedPSCBS() using 'multisession' futures ...
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Calling genotypes from normal allele B fractions...
     num [1:44010] 0.8274 0.5072 0.1671 0.1609 0.0421 ...
    Called genotypes:
     num [1:44010] 1 0.5 0 0 0 0 1 0 1 0.5 ...
     - attr(*, "modelFit")=List of 1
      ..$ :List of 7
      .. ..$ flavor             : chr "density"
      .. ..$ cn                 : int 2
      .. ..$ nbrOfGenotypeGroups: int 3
      .. ..$ tau                : num [1:2] 0.312 0.678
      .. ..$ n                  : int 43920
      .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. ..$ density: num [1:2] 0.465 0.496
      ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
    muN
        0   0.5     1 
    15627 12633 15750 
   Calling genotypes from normal allele B fractions...done
   Normalizing betaT using betaN (TumorBoost)...
    Normalized BAFs:
     num [1:44010] 0.9301 0.1667 0.0685 0.0995 0.0155 ...
     - attr(*, "modelFit")=List of 5
      ..$ method       : chr "normalizeTumorBoost"
      ..$ flavor       : chr "v4"
      ..$ delta        : num [1:44010] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. ..- attr(*, "modelFit")=List of 1
      .. .. ..$ :List of 7
      .. .. .. ..$ flavor             : chr "density"
      .. .. .. ..$ cn                 : int 2
      .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. .. ..$ n                  : int 43920
      .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      ..$ preserveScale: logi FALSE
      ..$ scaleFactor  : num NA
   Normalizing betaT using betaN (TumorBoost)...done
   Setup up data...
    'data.frame':	44010 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.9301 0.1667 0.0685 0.0995 0.0155 ...
      ..- attr(*, "modelFit")=List of 5
      .. ..$ method       : chr "normalizeTumorBoost"
      .. ..$ flavor       : chr "v4"
      .. ..$ delta        : num [1:44010] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. .. ..- attr(*, "modelFit")=List of 1
      .. .. .. ..$ :List of 7
      .. .. .. .. ..$ flavor             : chr "density"
      .. .. .. .. ..$ cn                 : int 2
      .. .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. .. .. ..$ n                  : int 43920
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      .. ..$ preserveScale: logi FALSE
      .. ..$ scaleFactor  : num NA
     $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.312 0.678
      .. .. ..$ n                  : int 43920
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. ..$ density: num [1:5] 1.622 0.465 1.125 0.496 1.587
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. ..$ density: num [1:2] 0.465 0.496
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 36
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	43974 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Segmenting multiple chromosomes...
    Number of chromosomes: 3
    Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
    Produced 3 seeds from this stream for future usage
    Chromosome #1 ('Chr01') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
    Chromosome #1 ('Chr01') of 3...done
    Chromosome #2 ('Chr02') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  14659 14660 14661 14662 14663 14664 14665 14666 14667 14668 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
    Chromosome #2 ('Chr02') of 3...done
    Chromosome #3 ('Chr03') of 3...
     'data.frame':	14658 obs. of  8 variables:
      $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
      $ x         : num  554484 730720 782343 878522 916294 ...
      $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
      $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
      $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
      $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
      $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      $ index     : int  29317 29318 29319 29320 29321 29322 29323 29324 29325 29326 ...
     Known segments:
     [1] chromosome start      end       
     <0 rows> (or 0-length row.names)
         Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
         Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 1 1 1
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 1
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.64 0 0.64 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          1    554484 143926517         7599  1.3859
       2          1 143926517 185449813         2668  2.0704
       3          1 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
       Number of heterozygous SNPs: 2120 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.06 0 0.06 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          1   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          1   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
       Number of loci: 2668
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.01 0 0.01 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          1 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          1 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 1
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 2 2 2 2 2 2 2 2 2 2 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 2 2 2
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 2
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.72 0 0.72 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
        Segmenting by CBS...done
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          2    554484 143926517         7599  1.3859
       2          2 143926517 185449813         2668  2.0704
       3          2 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 1
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 1
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
       Number of heterozygous SNPs: 2120 (100.00%)
        DH segmentation (locally-indexed) rows:
       Chromosome: 2
       Segmenting DH signals...
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
        Segmenting by CBS...
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         Chromosome: 2
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          1 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          1 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          1     1    1    554484 143926517         7599  1.3859         2120
      2          1     2    1 143926517 185449813         2668  2.0704          775
      3          1     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 1
      Chromosome 1 ('chr01') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr01') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1    554484 143926517         7599  1.3859         2120
     2          1     2    1 143926517 185449813         2668  2.0704          775
     3          1     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.06 0 0.06 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
    Chromosome #3 ('Chr03') of 3...done
         startRow endRow
       1       10   7594
    Merging (independently) segmented chromosome...
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          2   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          2   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
     Segmenting paired tumor-normal signals using Paired PSCBS...
       Number of TCN loci in segment: 2668
      Setup up data...
       Locus data for TCN segment:
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
      Setup up data...done
       Number of loci: 2668
      Ordering data along genome...
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 2
       Segmenting DH signals...
       'data.frame':	14658 obs. of  7 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
      Ordering data along genome...done
      Keeping only current chromosome for 'knownSegments'...
        Segmenting by CBS...
       Chromosome: 3
       Known segments for this chromosome:
       [1] chromosome start      end       
       <0 rows> (or 0-length row.names)
      Keeping only current chromosome for 'knownSegments'...done
      alphaTCN: 0.009
      alphaDH: 0.001
      Number of loci: 14658
         Chromosome: 2
      Calculating DHs...
       Number of SNPs: 14658
       Number of heterozygous SNPs: 4209 (28.71%)
       Normalized DHs:
        num [1:14658] NA NA NA NA NA ...
      Calculating DHs...done
      Random seed temporarily set (seed=c(10407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
      Produced 2 seeds from this stream for future usage
      Identification of change points by total copy numbers...
       Segmenting by CBS...
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Chromosome: 3
         Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.01 0 0.01 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          2 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          2 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  2 2 2 2 2 2 2 2 2 2 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 2
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 2
          Random seed temporarily set (seed=c(10407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 2 2 2 2 2 2 2 2 2 2 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 2
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 2
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.04 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          2 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          2 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          2     1    1    554484 143926517         7599  1.3859         2120
      2          2     2    1 143926517 185449813         2668  2.0704          775
      3          2     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 2
      Chromosome 1 ('chr02') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr02') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          2     1    1    554484 143926517         7599  1.3859         2120
     2          2     2    1 143926517 185449813         2668  2.0704          775
     3          2     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       Segmenting by CBS...done
       List of 4
        $ data   :'data.frame':	14658 obs. of  4 variables:
         ..$ chromosome: int [1:14658] 3 3 3 3 3 3 3 3 3 3 ...
         ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
         ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
         ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
        $ output :'data.frame':	3 obs. of  6 variables:
         ..$ sampleName: chr [1:3] NA NA NA
         ..$ chromosome: int [1:3] 3 3 3
         ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
         ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
         ..$ nbrOfLoci : int [1:3] 7599 2668 4391
         ..$ mean      : num [1:3] 1.39 2.07 2.63
        $ segRows:'data.frame':	3 obs. of  2 variables:
         ..$ startRow: int [1:3] 1 7600 10268
         ..$ endRow  : int [1:3] 7599 10267 14658
        $ params :List of 5
         ..$ alpha        : num 0.009
         ..$ undo         : num 0
         ..$ joinSegments : logi TRUE
         ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
         .. ..$ chromosome: int 3
         .. ..$ start     : num -Inf
         .. ..$ end       : num Inf
         ..$ seed         : int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.67 0 0.67 NA NA
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
        - attr(*, "randomSeed")= int [1:7] 10407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
      Identification of change points by total copy numbers...done
      Restructure TCN segmentation results...
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
       1          3    554484 143926517         7599  1.3859
       2          3 143926517 185449813         2668  2.0704
       3          3 185449813 247137334         4391  2.6341
       Number of TCN segments: 3
      Restructure TCN segmentation results...done
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...
       Number of TCN loci in segment: 7599
       Locus data for TCN segment:
       'data.frame':	7599 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  554484 730720 782343 878522 916294 ...
        $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
        $ betaT     : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
        $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
        $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
        $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
        $ rho       : num  NA NA NA NA NA ...
       Number of loci: 7599
       Number of SNPs: 2120 (27.90%)
       Number of heterozygous SNPs: 2120 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	7599 obs. of  4 variables:
          ..$ chromosome: int [1:7599] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
          ..$ y         : num [1:7599] NA NA NA NA NA ...
          ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 554484
          ..$ end       : num 1.44e+08
          ..$ nbrOfLoci : int 2120
          ..$ mean      : num 0.51
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 10
          ..$ endRow  : int 7594
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 554484
          .. ..$ end       : num 1.44e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.08 0 0.08 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       10   7594
         int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
        DH segmentation rows:
          startRow endRow
        1       10   7594
       Segmenting DH signals...done
       DH segmentation table:
         dhStart     dhEnd dhNbrOfLoci dhMean
       1  554484 143926517        2120 0.5101
         startRow endRow
       1       10   7594
       Rows:
       [1] 1
       TCN segmentation rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
         startRow endRow
       1       10   7594
       NULL
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
         startRow endRow
       1        1   7599
       Total CN segmentation table (expanded):
         chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       1          3   554484 143926517         7599  1.3859         2120         2120
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       1     1    1          3   554484 143926517         7599  1.3859         2120
         tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
       1         2120  554484 143926517        2120 0.5101
      Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
       'data.frame':	2668 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
        $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
        $ betaT     : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaTN    : num  0.027542 1.030222 1.094093 0.046763 -0.000948 ...
        $ betaN     : num  0.1624 0.8788 0.8225 0.1201 0.0317 ...
        $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
        $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
       Number of loci: 2668
       Number of SNPs: 775 (29.05%)
       Number of heterozygous SNPs: 775 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	2668 obs. of  4 variables:
          ..$ chromosome: int [1:2668] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
          ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
          ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 1.44e+08
          ..$ end       : num 1.85e+08
          ..$ nbrOfLoci : int 775
          ..$ mean      : num 0.097
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 15
          ..$ endRow  : int 2664
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 1.44e+08
          .. ..$ end       : num 1.85e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1       15   2664
         int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
        DH segmentation rows:
          startRow endRow
        1     7614  10263
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 143926517 185449813         775  0.097
         startRow endRow
       1     7614  10263
       Rows:
       [1] 2
       TCN segmentation rows:
         startRow endRow
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       2     7600  10267
         startRow endRow
       1     7614  10263
         startRow endRow
       1        1   7599
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
         startRow endRow
       1        1   7599
       2     7600  10267
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       2          3 143926517 185449813         2668  2.0704          775          775
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       2     2    1          3 143926517 185449813         2668  2.0704          775
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       2          775 143926517 185449813         775  0.097
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
       Number of TCN loci in segment: 4391
       Locus data for TCN segment:
       'data.frame':	4391 obs. of  9 variables:
        $ chromosome: int  3 3 3 3 3 3 3 3 3 3 ...
        $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
        $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
        $ betaT     : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
        $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
        $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
        $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        $ rho       : num  NA 0.2186 NA 0.0503 NA ...
       Number of loci: 4391
       Number of SNPs: 1314 (29.92%)
       Number of heterozygous SNPs: 1314 (100.00%)
       Chromosome: 3
       Segmenting DH signals...
        Segmenting by CBS...
         Chromosome: 3
          Random seed temporarily set (seed=c(10407, -1371615447, -889757879, 1692656974, -1723952224, 1378814990, 1816467252), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...done
        List of 4
         $ data   :'data.frame':	4391 obs. of  4 variables:
          ..$ chromosome: int [1:4391] 3 3 3 3 3 3 3 3 3 3 ...
          ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
          ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
          ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
         $ output :'data.frame':	1 obs. of  6 variables:
          ..$ sampleName: chr NA
          ..$ chromosome: int 3
          ..$ start     : num 1.85e+08
          ..$ end       : num 2.47e+08
          ..$ nbrOfLoci : int 1314
          ..$ mean      : num 0.23
         $ segRows:'data.frame':	1 obs. of  2 variables:
          ..$ startRow: int 2
          ..$ endRow  : int 4388
         $ params :List of 5
          ..$ alpha        : num 0.001
          ..$ undo         : num 0
          ..$ joinSegments : logi TRUE
          ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
          .. ..$ chromosome: int 3
          .. ..$ start     : num 1.85e+08
          .. ..$ end       : num 2.47e+08
          ..$ seed         : int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
         - attr(*, "randomSeed")= int [1:7] 10407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
        DH segmentation rows:
          startRow endRow
        1    10269  14655
       Segmenting DH signals...done
       DH segmentation table:
           dhStart     dhEnd dhNbrOfLoci dhMean
       1 185449813 247137334        1314 0.2295
         startRow endRow
       1    10269  14655
       Rows:
       [1] 3
       TCN segmentation rows:
         startRow endRow
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       3    10268  14658
         startRow endRow
       1    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       TCN segmentation (expanded) rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       TCN and DH segmentation rows:
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
         startRow endRow
       1       10   7594
       2     7614  10263
       3    10269  14655
         startRow endRow
       1        1   7599
       2     7600  10267
       3    10268  14658
       Total CN segmentation table (expanded):
         chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
       3          3 185449813 247137334         4391  2.6341         1314         1314
       (TCN,DH) segmentation for one total CN segment:
         tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
       3     3    1          3 185449813 247137334         4391  2.6341         1314
         tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
       3         1314 185449813 247137334        1314 0.2295
      Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
        chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
      1          3     1    1    554484 143926517         7599  1.3859         2120
      2          3     2    1 143926517 185449813         2668  2.0704          775
      3          3     3    1 185449813 247137334         4391  2.6341         1314
        tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
      1         2120    554484 143926517        2120 0.5101
      2          775 143926517 185449813         775 0.0970
      3         1314 185449813 247137334        1314 0.2295
      Calculating (C1,C2) per segment...
      Calculating (C1,C2) per segment...done
      Number of segments: 3
     Segmenting paired tumor-normal signals using Paired PSCBS...done
     Post-segmenting TCNs...
      Number of segments: 3
      Number of chromosomes: 1
      [1] 3
      Chromosome 1 ('chr03') of 1...
       Rows:
       [1] 1 2 3
       Number of segments: 3
       TCN segment #1 ('1') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #1 ('1') of 3...done
       TCN segment #2 ('2') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #2 ('2') of 3...done
       TCN segment #3 ('3') of 3...
        Nothing todo. Only one DH segmentation. Skipping.
       TCN segment #3 ('3') of 3...done
      Chromosome 1 ('chr03') of 1...done
      Update (C1,C2) per segment...
      Update (C1,C2) per segment...done
     Post-segmenting TCNs...done
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          3     1    1    554484 143926517         7599  1.3859         2120
     2          3     2    1 143926517 185449813         2668  2.0704          775
     3          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
     2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
     3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
     List of 5
      $ data      :Classes 'PairedPSCNData' and 'data.frame':	43974 obs. of  8 variables:
       ..$ chromosome: int [1:43974] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:43974] 554484 730720 782343 878522 916294 ...
       ..$ CT        : num [1:43974] 1.88 1.8 1.59 1.64 1.53 ...
       ..$ betaT     : num [1:43974] 0.0646 0.1672 0.9284 0.113 0.7209 ...
       ..$ betaTN    : num [1:43974] -0.0515 -0.1172 1.0194 0.031 0.8604 ...
       ..$ betaN     : num [1:43974] 0.116 0.284 0.909 0.082 0.86 ...
       ..$ muN       : num [1:43974] 0 0 1 0 1 1 1 0 1 0.5 ...
       ..$ rho       : num [1:43974] NA NA NA NA NA ...
      $ output    :Classes 'PairedPSCNSegments' and 'data.frame':	11 obs. of  15 variables:
       ..$ chromosome  : int [1:11] 1 1 1 NA 2 2 2 NA 3 3 ...
       ..$ tcnId       : int [1:11] 1 2 3 NA 1 2 3 NA 1 2 ...
       ..$ dhId        : int [1:11] 1 1 1 NA 1 1 1 NA 1 1 ...
       ..$ tcnStart    : num [1:11] 5.54e+05 1.44e+08 1.85e+08 NA 5.54e+05 ...
       ..$ tcnEnd      : num [1:11] 1.44e+08 1.85e+08 2.47e+08 NA 1.44e+08 ...
       ..$ tcnNbrOfLoci: int [1:11] 7599 2668 4391 NA 7599 2668 4391 NA 7599 2668 ...
       ..$ tcnMean     : num [1:11] 1.39 2.07 2.63 NA 1.39 ...
       ..$ tcnNbrOfSNPs: int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ tcnNbrOfHets: int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ dhStart     : num [1:11] 5.54e+05 1.44e+08 1.85e+08 NA 5.54e+05 ...
       ..$ dhEnd       : num [1:11] 1.44e+08 1.85e+08 2.47e+08 NA 1.44e+08 ...
       ..$ dhNbrOfLoci : int [1:11] 2120 775 1314 NA 2120 775 1314 NA 2120 775 ...
       ..$ dhMean      : num [1:11] 0.51 0.097 0.23 NA 0.51 ...
       ..$ c1Mean      : num [1:11] 0.339 0.935 1.015 NA 0.339 ...
       ..$ c2Mean      : num [1:11] 1.05 1.14 1.62 NA 1.05 ...
      $ tcnSegRows:'data.frame':	11 obs. of  2 variables:
       ..$ startRow: int [1:11] 1 7600 10268 NA 14659 22258 24926 NA 29317 36916 ...
       ..$ endRow  : int [1:11] 7599 10267 14658 NA 22257 24925 29316 NA 36915 39583 ...
      $ dhSegRows :'data.frame':	11 obs. of  2 variables:
       ..$ startRow: int [1:11] 10 7614 10269 NA 14668 22272 24927 NA 29326 36930 ...
       ..$ endRow  : int [1:11] 7594 10263 14655 NA 22252 24921 29313 NA 36910 39579 ...
      $ params    :List of 7
       ..$ alphaTCN     : num 0.009
       ..$ alphaDH      : num 0.001
       ..$ flavor       : chr "tcn&dh"
       ..$ tbn          : logi FALSE
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	0 obs. of  3 variables:
       .. ..$ chromosome: int(0) 
       .. ..$ start     : int(0) 
       .. ..$ end       : int(0) 
       ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
      - attr(*, "class")= chr [1:3] "PairedPSCBS" "PSCBS" "AbstractCBS"
    Merging (independently) segmented chromosome...done
      chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1          1     1    1    554484 143926517         7599  1.3859         2120
    2          1     2    1 143926517 185449813         2668  2.0704          775
    3          1     3    1 185449813 247137334         4391  2.6341         1314
    4         NA    NA   NA        NA        NA           NA      NA           NA
    5          2     1    1    554484 143926517         7599  1.3859         2120
    6          2     2    1 143926517 185449813         2668  2.0704          775
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
    1         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    2          775 143926517 185449813         775 0.0970 0.9347856 1.135614
    3         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    4           NA        NA        NA          NA     NA        NA       NA
    5         2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    6          775 143926517 185449813         775 0.0970 0.9347856 1.135614
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    6           2     2    1 143926517 185449813         2668  2.0704          775
    7           2     3    1 185449813 247137334         4391  2.6341         1314
    8          NA    NA   NA        NA        NA           NA      NA           NA
    9           3     1    1    554484 143926517         7599  1.3859         2120
    10          3     2    1 143926517 185449813         2668  2.0704          775
    11          3     3    1 185449813 247137334         4391  2.6341         1314
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
    6           775 143926517 185449813         775 0.0970 0.9347856 1.135614
    7          1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
    8            NA        NA        NA          NA     NA        NA       NA
    9          2120    554484 143926517        2120 0.5101 0.3394762 1.046424
    10          775 143926517 185449813         775 0.0970 0.9347856 1.135614
    11         1314 185449813 247137334        1314 0.2295 1.0147870 1.619313
   Segmenting multiple chromosomes...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  > 
  > message("*** segmentByPairedPSCBS() via futures ... DONE")
  *** segmentByPairedPSCBS() via futures ... DONE
  > 
  > 
  > message("*** segmentByPairedPSCBS() via futures with known segments ...")
  *** segmentByPairedPSCBS() via futures with known segments ...
  > fits <- list()
  > dataT <- subset(data, chromosome == 1)
  > gaps <- findLargeGaps(dataT, minLength=2e6)
  > knownSegments <- gapsToSegments(gaps)
  > 
  > for (strategy in strategies) {
  +   message(sprintf("- segmentByPairedPSCBS() w/ known segments using '%s' futures ...", strategy))
  +   plan(strategy)
  +   fit <- segmentByPairedPSCBS(dataT, knownSegments=knownSegments, seed=0xBEEF, verbose=TRUE)
  +   fits[[strategy]] <- fit
  +   equal <- all.equal(fit, fits[[1]])
  +   if (!equal) {
  +     str(fit)
  +     str(fits[[1]])
  +     print(equal)
  +     stop(sprintf("segmentByPairedPSCBS() w/ known segments using '%s' futures does not produce the same results as when using '%s' futures", strategy, names(fits)[1]))
  +   }
  + }
  - segmentByPairedPSCBS() w/ known segments using 'sequential' futures ...
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Calling genotypes from normal allele B fractions...
     num [1:14670] 0.8274 0.5072 0.1671 0.1609 0.0421 ...
    Called genotypes:
     num [1:14670] 1 0.5 0 0 0 0 1 0 1 0.5 ...
     - attr(*, "modelFit")=List of 1
      ..$ :List of 7
      .. ..$ flavor             : chr "density"
      .. ..$ cn                 : int 2
      .. ..$ nbrOfGenotypeGroups: int 3
      .. ..$ tau                : num [1:2] 0.315 0.677
      .. ..$ n                  : int 14640
      .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.551
      ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
    muN
       0  0.5    1 
    5221 4198 5251 
   Calling genotypes from normal allele B fractions...done
   Normalizing betaT using betaN (TumorBoost)...
    Normalized BAFs:
     num [1:14670] 0.9301 0.1667 0.0685 0.0995 0.0155 ...
     - attr(*, "modelFit")=List of 5
      ..$ method       : chr "normalizeTumorBoost"
      ..$ flavor       : chr "v4"
      ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. ..- attr(*, "modelFit")=List of 1
      .. .. ..$ :List of 7
      .. .. .. ..$ flavor             : chr "density"
      .. .. .. ..$ cn                 : int 2
      .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. ..$ n                  : int 14640
      .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      ..$ preserveScale: logi FALSE
      ..$ scaleFactor  : num NA
   Normalizing betaT using betaN (TumorBoost)...done
   Setup up data...
    'data.frame':	14670 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.9301 0.1667 0.0685 0.0995 0.0155 ...
      ..- attr(*, "modelFit")=List of 5
      .. ..$ method       : chr "normalizeTumorBoost"
      .. ..$ flavor       : chr "v4"
      .. ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. .. ..- attr(*, "modelFit")=List of 1
      .. .. .. ..$ :List of 7
      .. .. .. .. ..$ flavor             : chr "density"
      .. .. .. .. ..$ cn                 : int 2
      .. .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      .. ..$ preserveScale: logi FALSE
      .. ..$ scaleFactor  : num NA
     $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 12
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	14658 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Keeping only current chromosome for 'knownSegments'...
    Chromosome: 1
    Known segments for this chromosome:
      chromosome     start       end   length
    1          1      -Inf 120908858      Inf
    2          1 120908859 142693887 21785028
    3          1 142693888       Inf      Inf
   Keeping only current chromosome for 'knownSegments'...done
   alphaTCN: 0.009
   alphaDH: 0.001
   Number of loci: 14658
   Calculating DHs...
    Number of SNPs: 14658
    Number of heterozygous SNPs: 4196 (28.63%)
    Normalized DHs:
     num [1:14658] NA NA NA NA NA ...
   Calculating DHs...done
   Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
   Produced 2 seeds from this stream for future usage
   Identification of change points by total copy numbers...
    Segmenting by CBS...
     Chromosome: 1
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
      Produced 3 seeds from this stream for future usage
    Segmenting by CBS...done
    List of 4
     $ data   :'data.frame':	14658 obs. of  4 variables:
      ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
      ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
      ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
      ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
     $ output :'data.frame':	4 obs. of  6 variables:
      ..$ sampleName: chr [1:4] NA NA NA NA
      ..$ chromosome: int [1:4] 1 1 1 1
      ..$ start     : num [1:4] 5.54e+05 1.21e+08 1.43e+08 1.85e+08
      ..$ end       : num [1:4] 1.21e+08 1.43e+08 1.85e+08 2.47e+08
      ..$ nbrOfLoci : int [1:4] 7586 0 2681 4391
      ..$ mean      : num [1:4] 1.39 NA 2.07 2.63
     $ segRows:'data.frame':	4 obs. of  2 variables:
      ..$ startRow: int [1:4] 1 NA 7587 10268
      ..$ endRow  : int [1:4] 7586 NA 10267 14658
     $ params :List of 5
      ..$ alpha        : num 0.009
      ..$ undo         : num 0
      ..$ joinSegments : logi TRUE
      ..$ knownSegments:'data.frame':	4 obs. of  3 variables:
      .. ..$ chromosome: int [1:4] 1 1 2 1
      .. ..$ start     : num [1:4] -Inf -Inf -Inf 1.43e+08
      .. ..$ end       : num [1:4] 1.21e+08 Inf Inf Inf
      ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
     - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
     - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.27 0 0.27 NA NA
      ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
     - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
     - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
   Identification of change points by total copy numbers...done
   Restructure TCN segmentation results...
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
    1          1    554484 120908858         7586  1.3853
    2          1 120908859 142693887            0      NA
    3          1 142693888 185449813         2681  2.0689
    4          1 185449813 247137334         4391  2.6341
    Number of TCN segments: 4
   Restructure TCN segmentation results...done
   Total CN segment #1 ([    554484,1.20909e+08]) of 4...
    Number of TCN loci in segment: 7586
    Locus data for TCN segment:
    'data.frame':	7586 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
     $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     $ rho       : num  NA NA NA NA NA ...
    Number of loci: 7586
    Number of SNPs: 2108 (27.79%)
    Number of heterozygous SNPs: 2108 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	7586 obs. of  4 variables:
       ..$ chromosome: int [1:7586] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:7586] 554484 730720 782343 878522 916294 ...
       ..$ y         : num [1:7586] NA NA NA NA NA ...
       ..$ index     : int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 554484
       ..$ end       : num 1.21e+08
       ..$ nbrOfLoci : int 2108
       ..$ mean      : num 0.512
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 10
       ..$ endRow  : int 7574
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 554484
       .. ..$ end       : num 1.21e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.08 0 0.08 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       10   7574
      int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
     DH segmentation rows:
       startRow endRow
     1       10   7574
    Segmenting DH signals...done
    DH segmentation table:
      dhStart     dhEnd dhNbrOfLoci dhMean
    1  554484 120908858        2108 0.5116
      startRow endRow
    1       10   7574
    Rows:
    [1] 1
    TCN segmentation rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
      startRow endRow
    1       10   7574
    NULL
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
      startRow endRow
    1        1   7586
    Total CN segmentation table (expanded):
      chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    1          1   554484 120908858         7586  1.3853         2108         2108
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1     1    1          1   554484 120908858         7586  1.3853         2108
      tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
    1         2108  554484 120908858        2108 0.5116
   Total CN segment #1 ([    554484,1.20909e+08]) of 4...done
   Total CN segment #2 ([1.20909e+08,1.42694e+08]) of 4...
    Number of TCN loci in segment: 0
    Locus data for TCN segment:
    'data.frame':	0 obs. of  9 variables:
     $ chromosome: int 
     $ x         : num 
     $ CT        : num 
     $ betaT     : num 
     $ betaTN    : num 
     $ betaN     : num 
     $ muN       : num 
     $ index     : int 
     $ rho       : num 
    Number of loci: 0
    Number of SNPs: 0 (NaN%)
    Number of heterozygous SNPs: 0 (NaN%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: NA
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	0 obs. of  4 variables:
       ..$ chromosome: int(0) 
       ..$ x         : num(0) 
       ..$ y         : num(0) 
       ..$ index     : int(0) 
      $ output :'data.frame':	0 obs. of  6 variables:
       ..$ sampleName: chr(0) 
       ..$ chromosome: num(0) 
       ..$ start     : num(0) 
       ..$ end       : num(0) 
       ..$ nbrOfLoci : int(0) 
       ..$ mean      : num(0) 
      $ segRows:'data.frame':	0 obs. of  2 variables:
       ..$ startRow: int(0) 
       ..$ endRow  : int(0) 
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	0 obs. of  3 variables:
       .. ..$ chromosome: int(0) 
       .. ..$ start     : num(0) 
       .. ..$ end       : num(0) 
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0 0 0 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
      int(0) 
     DH segmentation rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
    Segmenting DH signals...done
    DH segmentation table:
       dhStart dhEnd dhNbrOfLoci dhMean
    NA      NA    NA          NA     NA
       startRow endRow
    NA       NA     NA
    Rows:
    [1] 2
    TCN segmentation rows:
      startRow endRow
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    2       NA     NA
       startRow endRow
    NA       NA     NA
      startRow endRow
    1        1   7586
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
      startRow endRow
    1        1   7586
    2       NA     NA
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    2          1 120908859 142693887            0      NA            0            0
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    2     2    1          1 120908859 142693887            0      NA            0
      tcnNbrOfHets dhStart dhEnd dhNbrOfLoci dhMean
    2            0      NA    NA          NA     NA
   Total CN segment #2 ([1.20909e+08,1.42694e+08]) of 4...done
   Total CN segment #3 ([1.42694e+08,1.8545e+08]) of 4...
    Number of TCN loci in segment: 2681
    Locus data for TCN segment:
    'data.frame':	2681 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
     $ CT        : num  2.27 1.55 1.47 1.5 1.81 ...
     $ betaT     : num  0.34 0.55 0.048 0.813 0.575 ...
     $ betaTN    : num  0.441 0.629 -0.05 0.958 0.872 ...
     $ betaN     : num  0.3851 0.3933 0.0981 0.8552 0.7028 ...
     $ muN       : num  0.5 0.5 0 1 1 1 1 0.5 1 1 ...
     $ index     : int  7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     $ rho       : num  0.117 0.258 NA NA NA ...
    Number of loci: 2681
    Number of SNPs: 777 (28.98%)
    Number of heterozygous SNPs: 777 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	2681 obs. of  4 variables:
       ..$ chromosome: int [1:2681] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:2681] 1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
       ..$ y         : num [1:2681] 0.117 0.258 NA NA NA ...
       ..$ index     : int [1:2681] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.43e+08
       ..$ end       : num 1.85e+08
       ..$ nbrOfLoci : int 777
       ..$ mean      : num 0.0973
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 1
       ..$ endRow  : int 2677
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.43e+08
       .. ..$ end       : num 1.85e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.02 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        1   2677
      int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     DH segmentation rows:
       startRow endRow
     1     7587  10263
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 142693888 185449813         777 0.0973
      startRow endRow
    1     7587  10263
    Rows:
    [1] 3
    TCN segmentation rows:
      startRow endRow
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    3     7587  10267
      startRow endRow
    1     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    3          1 142693888 185449813         2681  2.0689          777          777
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    3     3    1          1 142693888 185449813         2681  2.0689          777
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    3          777 142693888 185449813         777 0.0973
   Total CN segment #3 ([1.42694e+08,1.8545e+08]) of 4...done
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...
    Number of TCN loci in segment: 4391
    Locus data for TCN segment:
    'data.frame':	4391 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
     $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
     $ betaT     : num  0.0811 0.5154 0.9473 0.3734 0.7506 ...
     $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
     $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
     $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
     $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     $ rho       : num  NA 0.2186 NA 0.0503 NA ...
    Number of loci: 4391
    Number of SNPs: 1311 (29.86%)
    Number of heterozygous SNPs: 1311 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	4391 obs. of  4 variables:
       ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
       ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
       ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.85e+08
       ..$ end       : num 2.47e+08
       ..$ nbrOfLoci : int 1311
       ..$ mean      : num 0.23
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 2
       ..$ endRow  : int 4388
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.85e+08
       .. ..$ end       : num 2.47e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        2   4388
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     DH segmentation rows:
       startRow endRow
     1    10269  14655
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 185449813 247137334        1311 0.2295
      startRow endRow
    1    10269  14655
    Rows:
    [1] 4
    TCN segmentation rows:
      startRow endRow
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    4    10268  14658
      startRow endRow
    1    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
    4    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    4          1 185449813 247137334         4391  2.6341         1311         1311
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    4     4    1          1 185449813 247137334         4391  2.6341         1311
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    4         1311 185449813 247137334        1311 0.2295
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...done
     chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
   1          1     1    1    554484 120908858         7586  1.3853         2108
   2          1     2    1 120908859 142693887            0      NA            0
   3          1     3    1 142693888 185449813         2681  2.0689          777
   4          1     4    1 185449813 247137334         4391  2.6341         1311
     tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
   1         2108    554484 120908858        2108 0.5116
   2            0        NA        NA          NA     NA
   3          777 142693888 185449813         777 0.0973
   4         1311 185449813 247137334        1311 0.2295
   Calculating (C1,C2) per segment...
   Calculating (C1,C2) per segment...done
   Number of segments: 4
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Post-segmenting TCNs...
   Number of segments: 4
   Number of chromosomes: 1
   [1] 1
   Chromosome 1 ('chr01') of 1...
    Rows:
    [1] 1 2 3 4
    Number of segments: 4
    TCN segment #1 ('1') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #1 ('1') of 4...done
    TCN segment #2 ('2') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #2 ('2') of 4...done
    TCN segment #3 ('3') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #3 ('3') of 4...done
    TCN segment #4 ('4') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #4 ('4') of 4...done
   Chromosome 1 ('chr01') of 1...done
   Update (C1,C2) per segment...
   Update (C1,C2) per segment...done
  Post-segmenting TCNs...done
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120908858         7586  1.3853         2108
  2          1     2    1 120908859 142693887            0      NA            0
  3          1     3    1 142693888 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120908858        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 142693888 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120908858         7586  1.3853         2108
  2          1     2    1 120908859 142693887            0      NA            0
  3          1     3    1 142693888 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120908858        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 142693888 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
  - segmentByPairedPSCBS() w/ known segments using 'multisession' futures ...
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Calling genotypes from normal allele B fractions...
     num [1:14670] 0.8274 0.5072 0.1671 0.1609 0.0421 ...
    Called genotypes:
     num [1:14670] 1 0.5 0 0 0 0 1 0 1 0.5 ...
     - attr(*, "modelFit")=List of 1
      ..$ :List of 7
      .. ..$ flavor             : chr "density"
      .. ..$ cn                 : int 2
      .. ..$ nbrOfGenotypeGroups: int 3
      .. ..$ tau                : num [1:2] 0.315 0.677
      .. ..$ n                  : int 14640
      .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.551
      ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
    muN
       0  0.5    1 
    5221 4198 5251 
   Calling genotypes from normal allele B fractions...done
   Normalizing betaT using betaN (TumorBoost)...
    Normalized BAFs:
     num [1:14670] 0.9301 0.1667 0.0685 0.0995 0.0155 ...
     - attr(*, "modelFit")=List of 5
      ..$ method       : chr "normalizeTumorBoost"
      ..$ flavor       : chr "v4"
      ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. ..- attr(*, "modelFit")=List of 1
      .. .. ..$ :List of 7
      .. .. .. ..$ flavor             : chr "density"
      .. .. .. ..$ cn                 : int 2
      .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. ..$ n                  : int 14640
      .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      ..$ preserveScale: logi FALSE
      ..$ scaleFactor  : num NA
   Normalizing betaT using betaN (TumorBoost)...done
   Setup up data...
    'data.frame':	14670 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.9301 0.1667 0.0685 0.0995 0.0155 ...
      ..- attr(*, "modelFit")=List of 5
      .. ..$ method       : chr "normalizeTumorBoost"
      .. ..$ flavor       : chr "v4"
      .. ..$ delta        : num [1:14670] -0.17264 0.00239 0.1671 0.16085 0.04213 ...
      .. .. ..- attr(*, "modelFit")=List of 1
      .. .. .. ..$ :List of 7
      .. .. .. .. ..$ flavor             : chr "density"
      .. .. .. .. ..$ cn                 : int 2
      .. .. .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. .. .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
      .. ..$ preserveScale: logi FALSE
      .. ..$ scaleFactor  : num NA
     $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 12
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	14658 obs. of  7 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Keeping only current chromosome for 'knownSegments'...
    Chromosome: 1
    Known segments for this chromosome:
      chromosome     start       end   length
    1          1      -Inf 120908858      Inf
    2          1 120908859 142693887 21785028
    3          1 142693888       Inf      Inf
   Keeping only current chromosome for 'knownSegments'...done
   alphaTCN: 0.009
   alphaDH: 0.001
   Number of loci: 14658
   Calculating DHs...
    Number of SNPs: 14658
    Number of heterozygous SNPs: 4196 (28.63%)
    Normalized DHs:
     num [1:14658] NA NA NA NA NA ...
   Calculating DHs...done
   Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
   Produced 2 seeds from this stream for future usage
   Identification of change points by total copy numbers...
    Segmenting by CBS...
     Chromosome: 1
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
      Produced 3 seeds from this stream for future usage
    Segmenting by CBS...done
    List of 4
     $ data   :'data.frame':	14658 obs. of  4 variables:
      ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
      ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
      ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
      ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
     $ output :'data.frame':	4 obs. of  6 variables:
      ..$ sampleName: chr [1:4] NA NA NA NA
      ..$ chromosome: int [1:4] 1 1 1 1
      ..$ start     : num [1:4] 5.54e+05 1.21e+08 1.43e+08 1.85e+08
      ..$ end       : num [1:4] 1.21e+08 1.43e+08 1.85e+08 2.47e+08
      ..$ nbrOfLoci : int [1:4] 7586 0 2681 4391
      ..$ mean      : num [1:4] 1.39 NA 2.07 2.63
     $ segRows:'data.frame':	4 obs. of  2 variables:
      ..$ startRow: int [1:4] 1 NA 7587 10268
      ..$ endRow  : int [1:4] 7586 NA 10267 14658
     $ params :List of 5
      ..$ alpha        : num 0.009
      ..$ undo         : num 0
      ..$ joinSegments : logi TRUE
      ..$ knownSegments:'data.frame':	4 obs. of  3 variables:
      .. ..$ chromosome: int [1:4] 1 1 2 1
      .. ..$ start     : num [1:4] -Inf -Inf -Inf 1.43e+08
      .. ..$ end       : num [1:4] 1.21e+08 Inf Inf Inf
      ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
     - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
     - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.25 0 0.25 NA NA
      ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
     - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
     - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
   Identification of change points by total copy numbers...done
   Restructure TCN segmentation results...
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
    1          1    554484 120908858         7586  1.3853
    2          1 120908859 142693887            0      NA
    3          1 142693888 185449813         2681  2.0689
    4          1 185449813 247137334         4391  2.6341
    Number of TCN segments: 4
   Restructure TCN segmentation results...done
   Total CN segment #1 ([    554484,1.20909e+08]) of 4...
    Number of TCN loci in segment: 7586
    Locus data for TCN segment:
    'data.frame':	7586 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  -0.0515 -0.1172 1.0194 0.031 0.8604 ...
     $ betaN     : num  0.116 0.284 0.909 0.082 0.86 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
     $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     $ rho       : num  NA NA NA NA NA ...
    Number of loci: 7586
    Number of SNPs: 2108 (27.79%)
    Number of heterozygous SNPs: 2108 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	7586 obs. of  4 variables:
       ..$ chromosome: int [1:7586] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:7586] 554484 730720 782343 878522 916294 ...
       ..$ y         : num [1:7586] NA NA NA NA NA ...
       ..$ index     : int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 554484
       ..$ end       : num 1.21e+08
       ..$ nbrOfLoci : int 2108
       ..$ mean      : num 0.512
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 10
       ..$ endRow  : int 7574
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 554484
       .. ..$ end       : num 1.21e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.13 0 0.12 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       10   7574
      int [1:7586] 1 2 3 4 5 6 7 8 9 10 ...
     DH segmentation rows:
       startRow endRow
     1       10   7574
    Segmenting DH signals...done
    DH segmentation table:
      dhStart     dhEnd dhNbrOfLoci dhMean
    1  554484 120908858        2108 0.5116
      startRow endRow
    1       10   7574
    Rows:
    [1] 1
    TCN segmentation rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
      startRow endRow
    1       10   7574
    NULL
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
      startRow endRow
    1        1   7586
    Total CN segmentation table (expanded):
      chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    1          1   554484 120908858         7586  1.3853         2108         2108
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1     1    1          1   554484 120908858         7586  1.3853         2108
      tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
    1         2108  554484 120908858        2108 0.5116
   Total CN segment #1 ([    554484,1.20909e+08]) of 4...done
   Total CN segment #2 ([1.20909e+08,1.42694e+08]) of 4...
    Number of TCN loci in segment: 0
    Locus data for TCN segment:
    'data.frame':	0 obs. of  9 variables:
     $ chromosome: int 
     $ x         : num 
     $ CT        : num 
     $ betaT     : num 
     $ betaTN    : num 
     $ betaN     : num 
     $ muN       : num 
     $ index     : int 
     $ rho       : num 
    Number of loci: 0
    Number of SNPs: 0 (NaN%)
    Number of heterozygous SNPs: 0 (NaN%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: NA
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	0 obs. of  4 variables:
       ..$ chromosome: int(0) 
       ..$ x         : num(0) 
       ..$ y         : num(0) 
       ..$ index     : int(0) 
      $ output :'data.frame':	0 obs. of  6 variables:
       ..$ sampleName: chr(0) 
       ..$ chromosome: num(0) 
       ..$ start     : num(0) 
       ..$ end       : num(0) 
       ..$ nbrOfLoci : int(0) 
       ..$ mean      : num(0) 
      $ segRows:'data.frame':	0 obs. of  2 variables:
       ..$ startRow: int(0) 
       ..$ endRow  : int(0) 
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	0 obs. of  3 variables:
       .. ..$ chromosome: int(0) 
       .. ..$ start     : num(0) 
       .. ..$ end       : num(0) 
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0 0 0 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
      int(0) 
     DH segmentation rows:
     [1] startRow endRow  
     <0 rows> (or 0-length row.names)
    Segmenting DH signals...done
    DH segmentation table:
       dhStart dhEnd dhNbrOfLoci dhMean
    NA      NA    NA          NA     NA
       startRow endRow
    NA       NA     NA
    Rows:
    [1] 2
    TCN segmentation rows:
      startRow endRow
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    2       NA     NA
       startRow endRow
    NA       NA     NA
      startRow endRow
    1        1   7586
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
      startRow endRow
    1        1   7586
    2       NA     NA
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    2          1 120908859 142693887            0      NA            0            0
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    2     2    1          1 120908859 142693887            0      NA            0
      tcnNbrOfHets dhStart dhEnd dhNbrOfLoci dhMean
    2            0      NA    NA          NA     NA
   Total CN segment #2 ([1.20909e+08,1.42694e+08]) of 4...done
   Total CN segment #3 ([1.42694e+08,1.8545e+08]) of 4...
    Number of TCN loci in segment: 2681
    Locus data for TCN segment:
    'data.frame':	2681 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
     $ CT        : num  2.27 1.55 1.47 1.5 1.81 ...
     $ betaT     : num  0.34 0.55 0.048 0.813 0.575 ...
     $ betaTN    : num  0.441 0.629 -0.05 0.958 0.872 ...
     $ betaN     : num  0.3851 0.3933 0.0981 0.8552 0.7028 ...
     $ muN       : num  0.5 0.5 0 1 1 1 1 0.5 1 1 ...
     $ index     : int  7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     $ rho       : num  0.117 0.258 NA NA NA ...
    Number of loci: 2681
    Number of SNPs: 777 (28.98%)
    Number of heterozygous SNPs: 777 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	2681 obs. of  4 variables:
       ..$ chromosome: int [1:2681] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:2681] 1.43e+08 1.43e+08 1.43e+08 1.43e+08 1.44e+08 ...
       ..$ y         : num [1:2681] 0.117 0.258 NA NA NA ...
       ..$ index     : int [1:2681] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.43e+08
       ..$ end       : num 1.85e+08
       ..$ nbrOfLoci : int 777
       ..$ mean      : num 0.0973
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 1
       ..$ endRow  : int 2677
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.43e+08
       .. ..$ end       : num 1.85e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.01 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        1   2677
      int [1:2681] 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 ...
     DH segmentation rows:
       startRow endRow
     1     7587  10263
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 142693888 185449813         777 0.0973
      startRow endRow
    1     7587  10263
    Rows:
    [1] 3
    TCN segmentation rows:
      startRow endRow
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    3     7587  10267
      startRow endRow
    1     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    3          1 142693888 185449813         2681  2.0689          777          777
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    3     3    1          1 142693888 185449813         2681  2.0689          777
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    3          777 142693888 185449813         777 0.0973
   Total CN segment #3 ([1.42694e+08,1.8545e+08]) of 4...done
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...
    Number of TCN loci in segment: 4391
    Locus data for TCN segment:
    'data.frame':	4391 obs. of  9 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
     $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
     $ betaT     : num  0.0811 0.5154 0.9473 0.3734 0.7506 ...
     $ betaTN    : num  -0.169 0.609 1.028 0.525 0.968 ...
     $ betaN     : num  0.25 0.38 0.919 0.34 0.783 ...
     $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
     $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     $ rho       : num  NA 0.2186 NA 0.0503 NA ...
    Number of loci: 4391
    Number of SNPs: 1311 (29.86%)
    Number of heterozygous SNPs: 1311 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	4391 obs. of  4 variables:
       ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
       ..$ y         : num [1:4391] NA 0.2186 NA 0.0503 NA ...
       ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.85e+08
       ..$ end       : num 2.47e+08
       ..$ nbrOfLoci : int 1311
       ..$ mean      : num 0.23
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 2
       ..$ endRow  : int 4388
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.85e+08
       .. ..$ end       : num 2.47e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.03 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        2   4388
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     DH segmentation rows:
       startRow endRow
     1    10269  14655
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 185449813 247137334        1311 0.2295
      startRow endRow
    1    10269  14655
    Rows:
    [1] 4
    TCN segmentation rows:
      startRow endRow
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    4    10268  14658
      startRow endRow
    1    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
      startRow endRow
    1       10   7574
    2       NA     NA
    3     7587  10263
    4    10269  14655
      startRow endRow
    1        1   7586
    2       NA     NA
    3     7587  10267
    4    10268  14658
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    4          1 185449813 247137334         4391  2.6341         1311         1311
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    4     4    1          1 185449813 247137334         4391  2.6341         1311
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    4         1311 185449813 247137334        1311 0.2295
   Total CN segment #4 ([1.8545e+08,2.47137e+08]) of 4...done
     chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
   1          1     1    1    554484 120908858         7586  1.3853         2108
   2          1     2    1 120908859 142693887            0      NA            0
   3          1     3    1 142693888 185449813         2681  2.0689          777
   4          1     4    1 185449813 247137334         4391  2.6341         1311
     tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
   1         2108    554484 120908858        2108 0.5116
   2            0        NA        NA          NA     NA
   3          777 142693888 185449813         777 0.0973
   4         1311 185449813 247137334        1311 0.2295
   Calculating (C1,C2) per segment...
   Calculating (C1,C2) per segment...done
   Number of segments: 4
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Post-segmenting TCNs...
   Number of segments: 4
   Number of chromosomes: 1
   [1] 1
   Chromosome 1 ('chr01') of 1...
    Rows:
    [1] 1 2 3 4
    Number of segments: 4
    TCN segment #1 ('1') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #1 ('1') of 4...done
    TCN segment #2 ('2') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #2 ('2') of 4...done
    TCN segment #3 ('3') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #3 ('3') of 4...done
    TCN segment #4 ('4') of 4...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #4 ('4') of 4...done
   Chromosome 1 ('chr01') of 1...done
   Update (C1,C2) per segment...
   Update (C1,C2) per segment...done
  Post-segmenting TCNs...done
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120908858         7586  1.3853         2108
  2          1     2    1 120908859 142693887            0      NA            0
  3          1     3    1 142693888 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120908858        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 142693888 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 120908858         7586  1.3853         2108
  2          1     2    1 120908859 142693887            0      NA            0
  3          1     3    1 142693888 185449813         2681  2.0689          777
  4          1     4    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2108    554484 120908858        2108 0.5116 0.3382903 1.047010
  2            0        NA        NA          NA     NA        NA       NA
  3          777 142693888 185449813         777 0.0973 0.9337980 1.135102
  4         1311 185449813 247137334        1311 0.2295 1.0147870 1.619313
  > 
  > message("*** segmentByPairedPSCBS() via futures ... DONE")
  *** segmentByPairedPSCBS() via futures ... DONE
  > 
  > 
  > ## Cleanup
  > plan(oplan)
  > rm(list=c("fits", "data", "fit"))
  > 
  > proc.time()
     user  system elapsed 
     8.46    0.54   16.15 
Running the tests in 'tests/segmentByPairedPSCBS,noNormalBAFs.R' failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.67.0 (2024-02-17 19:10:02 UTC) successfully loaded. See ?PSCBS for help.
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Load SNP microarray data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data <- PSCBS::exampleData("paired.chr01")
  > str(data)
  'data.frame':	73346 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2224111 2319424 2543484 2926730 2941694 3084986 3155127 3292731 3695086 ...
   $ CT        : num  1.625 1.071 1.406 1.18 0.856 ...
   $ betaT     : num  0.757 0.771 0.834 0.778 0.229 ...
   $ CN        : num  2.36 2.13 2.59 1.93 1.71 ...
   $ betaN     : num  0.827 0.875 0.887 0.884 0.103 ...
  > 
  > # Drop single-locus outliers
  > dataS <- dropSegmentationOutliers(data)
  > 
  > # Run light-weight tests by default
  > if (Sys.getenv("_R_CHECK_FULL_") == "") {
  +   # Use only every 5th data point
  +   dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  +   # Number of segments (for assertion)
  +   nSegs <- 3L
  +   # Number of bootstrap samples (see below)
  +   B <- 100L
  + } else {
  +   # Full tests
  +   nSegs <- 8L
  +   B <- 1000L
  + }
  > 
  > str(dataS)
  'data.frame':	14670 obs. of  6 variables:
   $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
   $ x         : int  1145994 2941694 3710825 4240737 4276892 4464544 4714611 5095111 5034491 5158416 ...
   $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
   $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
   $ CN        : num  2.36 2.13 2.26 2.01 2.32 ...
   $ betaN     : num  0.8274 0.5072 0.1671 0.1609 0.0421 ...
  > 
  > R.oo::attachLocally(dataS)
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Simulate that genotypes are known by other means
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > library("aroma.light")
  aroma.light v3.32.0 (2023-10-24) successfully loaded. See ?aroma.light for help.
  > muN <- aroma.light::callNaiveGenotypes(betaN, censorAt=c(0,1))
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Paired PSCBS segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- segmentByPairedPSCBS(CT, betaT=betaT, muN=muN, tbn=FALSE,
  +                             chromosome=chromosome, x=x,
  +                             seed=0xBEEF, verbose=-10)
  Segmenting paired tumor-normal signals using Paired PSCBS...
   Setup up data...
    'data.frame':	14670 obs. of  6 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1145994 2941694 3710825 4240737 4276892 ...
     $ CT        : num  1.63 1.4 1.41 1.17 1.16 ...
     $ betaT     : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ betaTN    : num  0.7574 0.169 0.2357 0.2604 0.0576 ...
     $ muN       : num  1 0.5 0 0 0 0 1 0 1 0.5 ...
      ..- attr(*, "modelFit")=List of 1
      .. ..$ :List of 7
      .. .. ..$ flavor             : chr "density"
      .. .. ..$ cn                 : int 2
      .. .. ..$ nbrOfGenotypeGroups: int 3
      .. .. ..$ tau                : num [1:2] 0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:5] "peak" "valley" "peak" "valley" ...
      .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num [1:5] 1.479 0.522 1.056 0.551 1.453
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : chr [1:2] "valley" "valley"
      .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. ..$ density: num [1:2] 0.522 0.551
      .. ..- attr(*, "class")= chr [1:2] "NaiveGenotypeModelFit" "list"
   Setup up data...done
   Dropping loci for which TCNs are missing...
    Number of loci dropped: 12
   Dropping loci for which TCNs are missing...done
   Ordering data along genome...
    'data.frame':	14658 obs. of  6 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
   Ordering data along genome...done
   Keeping only current chromosome for 'knownSegments'...
    Chromosome: 1
    Known segments for this chromosome:
    [1] chromosome start      end       
    <0 rows> (or 0-length row.names)
   Keeping only current chromosome for 'knownSegments'...done
   alphaTCN: 0.009
   alphaDH: 0.001
   Number of loci: 14658
   Calculating DHs...
    Number of SNPs: 14658
    Number of heterozygous SNPs: 4196 (28.63%)
    Normalized DHs:
     num [1:14658] NA NA NA NA NA ...
   Calculating DHs...done
   Random seed temporarily set (seed=c(48879), kind="L'Ecuyer-CMRG")
   Produced 2 seeds from this stream for future usage
   Identification of change points by total copy numbers...
    Segmenting by CBS...
     Chromosome: 1
      Random seed temporarily set (seed=c(10407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
    Segmenting by CBS...done
    List of 4
     $ data   :'data.frame':	14658 obs. of  4 variables:
      ..$ chromosome: int [1:14658] 1 1 1 1 1 1 1 1 1 1 ...
      ..$ x         : num [1:14658] 554484 730720 782343 878522 916294 ...
      ..$ y         : num [1:14658] 1.88 1.8 1.59 1.64 1.53 ...
      ..$ index     : int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
     $ output :'data.frame':	3 obs. of  6 variables:
      ..$ sampleName: chr [1:3] NA NA NA
      ..$ chromosome: int [1:3] 1 1 1
      ..$ start     : num [1:3] 5.54e+05 1.44e+08 1.85e+08
      ..$ end       : num [1:3] 1.44e+08 1.85e+08 2.47e+08
      ..$ nbrOfLoci : int [1:3] 7599 2668 4391
      ..$ mean      : num [1:3] 1.39 2.07 2.63
     $ segRows:'data.frame':	3 obs. of  2 variables:
      ..$ startRow: int [1:3] 1 7600 10268
      ..$ endRow  : int [1:3] 7599 10267 14658
     $ params :List of 5
      ..$ alpha        : num 0.009
      ..$ undo         : num 0
      ..$ joinSegments : logi TRUE
      ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
      .. ..$ chromosome: int 1
      .. ..$ start     : num -Inf
      .. ..$ end       : num Inf
      ..$ seed         : int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
     - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
     - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.64 0.01 0.65 NA NA
      ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
     - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
     - attr(*, "randomSeed")= int [1:7] 10407 1066287653 -51199871 161854402 -1995183193 1503453565 -747102133
   Identification of change points by total copy numbers...done
   Restructure TCN segmentation results...
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean
    1          1    554484 143926517         7599  1.3859
    2          1 143926517 185449813         2668  2.0704
    3          1 185449813 247137334         4391  2.6341
    Number of TCN segments: 3
   Restructure TCN segmentation results...done
   Total CN segment #1 ([    554484,1.43927e+08]) of 3...
    Number of TCN loci in segment: 7599
    Locus data for TCN segment:
    'data.frame':	7599 obs. of  8 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  554484 730720 782343 878522 916294 ...
     $ CT        : num  1.88 1.8 1.59 1.64 1.53 ...
     $ betaT     : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ betaTN    : num  0.0646 0.1672 0.9284 0.113 0.7209 ...
     $ muN       : num  0 0 1 0 1 1 1 0 1 0.5 ...
     $ index     : int  1 2 3 4 5 6 7 8 9 10 ...
     $ rho       : num  NA NA NA NA NA ...
    Number of loci: 7599
    Number of SNPs: 2111 (27.78%)
    Number of heterozygous SNPs: 2111 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	7599 obs. of  4 variables:
       ..$ chromosome: int [1:7599] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:7599] 554484 730720 782343 878522 916294 ...
       ..$ y         : num [1:7599] NA NA NA NA NA ...
       ..$ index     : int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 554484
       ..$ end       : num 1.44e+08
       ..$ nbrOfLoci : int 2111
       ..$ mean      : num 0.524
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 10
       ..$ endRow  : int 7594
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 554484
       .. ..$ end       : num 1.44e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.07 0 0.07 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       10   7594
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     DH segmentation rows:
       startRow endRow
     1       10   7594
    Segmenting DH signals...done
    DH segmentation table:
      dhStart     dhEnd dhNbrOfLoci dhMean
    1  554484 143926517        2111 0.5237
      startRow endRow
    1       10   7594
    Rows:
    [1] 1
    TCN segmentation rows:
      startRow endRow
    1        1   7599
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
      startRow endRow
    1       10   7594
    NULL
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
      startRow endRow
    1        1   7599
    Total CN segmentation table (expanded):
      chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    1          1   554484 143926517         7599  1.3859         2111         2111
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    1     1    1          1   554484 143926517         7599  1.3859         2111
      tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean
    1         2111  554484 143926517        2111 0.5237
   Total CN segment #1 ([    554484,1.43927e+08]) of 3...done
   Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
    Number of TCN loci in segment: 2668
    Locus data for TCN segment:
    'data.frame':	2668 obs. of  8 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
     $ CT        : num  2.1 2.1 2.09 1.8 2.34 ...
     $ betaT     : num  0.1899 0.909 0.9166 0.1668 0.0308 ...
     $ betaTN    : num  0.1899 0.909 0.9166 0.1668 0.0308 ...
     $ muN       : num  0 1 1 0 0 1 0 0 0 0 ...
     $ index     : int  7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     $ rho       : num  NA NA NA NA NA NA NA NA NA NA ...
    Number of loci: 2668
    Number of SNPs: 774 (29.01%)
    Number of heterozygous SNPs: 774 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	2668 obs. of  4 variables:
       ..$ chromosome: int [1:2668] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:2668] 1.44e+08 1.44e+08 1.44e+08 1.44e+08 1.44e+08 ...
       ..$ y         : num [1:2668] NA NA NA NA NA NA NA NA NA NA ...
       ..$ index     : int [1:2668] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.44e+08
       ..$ end       : num 1.85e+08
       ..$ nbrOfLoci : int 774
       ..$ mean      : num 0.154
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 15
       ..$ endRow  : int 2664
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.44e+08
       .. ..$ end       : num 1.85e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.02 0 0.02 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1       15   2664
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     DH segmentation rows:
       startRow endRow
     1     7614  10263
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 143926517 185449813         774 0.1542
      startRow endRow
    1     7614  10263
    Rows:
    [1] 2
    TCN segmentation rows:
      startRow endRow
    2     7600  10267
    TCN and DH segmentation rows:
      startRow endRow
    2     7600  10267
      startRow endRow
    1     7614  10263
      startRow endRow
    1        1   7599
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
    2     7614  10263
      startRow endRow
    1        1   7599
    2     7600  10267
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    2          1 143926517 185449813         2668  2.0704          774          774
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    2     2    1          1 143926517 185449813         2668  2.0704          774
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    2          774 143926517 185449813         774 0.1542
   Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...done
   Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...
    Number of TCN loci in segment: 4391
    Locus data for TCN segment:
    'data.frame':	4391 obs. of  8 variables:
     $ chromosome: int  1 1 1 1 1 1 1 1 1 1 ...
     $ x         : num  1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
     $ CT        : num  2.93 2.15 2.82 2.93 2.46 ...
     $ betaT     : num  0.0811 0.5154 0.9473 0.3734 0.7506 ...
     $ betaTN    : num  0.0811 0.5154 0.9473 0.3734 0.7506 ...
     $ muN       : num  0 0.5 1 0.5 1 1 0 1 0 1 ...
     $ index     : int  10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     $ rho       : num  NA 0.0308 NA 0.2533 NA ...
    Number of loci: 4391
    Number of SNPs: 1311 (29.86%)
    Number of heterozygous SNPs: 1311 (100.00%)
    Chromosome: 1
    Segmenting DH signals...
     Segmenting by CBS...
      Chromosome: 1
       Random seed temporarily set (seed=c(10407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     List of 4
      $ data   :'data.frame':	4391 obs. of  4 variables:
       ..$ chromosome: int [1:4391] 1 1 1 1 1 1 1 1 1 1 ...
       ..$ x         : num [1:4391] 1.85e+08 1.85e+08 1.85e+08 1.86e+08 1.86e+08 ...
       ..$ y         : num [1:4391] NA 0.0308 NA 0.2533 NA ...
       ..$ index     : int [1:4391] 1 2 3 4 5 6 7 8 9 10 ...
      $ output :'data.frame':	1 obs. of  6 variables:
       ..$ sampleName: chr NA
       ..$ chromosome: int 1
       ..$ start     : num 1.85e+08
       ..$ end       : num 2.47e+08
       ..$ nbrOfLoci : int 1311
       ..$ mean      : num 0.251
      $ segRows:'data.frame':	1 obs. of  2 variables:
       ..$ startRow: int 2
       ..$ endRow  : int 4388
      $ params :List of 5
       ..$ alpha        : num 0.001
       ..$ undo         : num 0
       ..$ joinSegments : logi TRUE
       ..$ knownSegments:'data.frame':	1 obs. of  3 variables:
       .. ..$ chromosome: int 1
       .. ..$ start     : num 1.85e+08
       .. ..$ end       : num 2.47e+08
       ..$ seed         : int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
      - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.05 0 0.05 NA NA
       ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
      - attr(*, "pkgDetails")= chr "DNAcopy v1.76.0"
      - attr(*, "randomSeed")= int [1:7] 10407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
     DH segmentation (locally-indexed) rows:
       startRow endRow
     1        2   4388
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     DH segmentation rows:
       startRow endRow
     1    10269  14655
    Segmenting DH signals...done
    DH segmentation table:
        dhStart     dhEnd dhNbrOfLoci dhMean
    1 185449813 247137334        1311 0.2512
      startRow endRow
    1    10269  14655
    Rows:
    [1] 3
    TCN segmentation rows:
      startRow endRow
    3    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    3    10268  14658
      startRow endRow
    1    10269  14655
      startRow endRow
    1        1   7599
    2     7600  10267
    TCN segmentation (expanded) rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
    TCN and DH segmentation rows:
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
      startRow endRow
    1       10   7594
    2     7614  10263
    3    10269  14655
      startRow endRow
    1        1   7599
    2     7600  10267
    3    10268  14658
    Total CN segmentation table (expanded):
      chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs tcnNbrOfHets
    3          1 185449813 247137334         4391  2.6341         1311         1311
    (TCN,DH) segmentation for one total CN segment:
      tcnId dhId chromosome  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
    3     3    1          1 185449813 247137334         4391  2.6341         1311
      tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
    3         1311 185449813 247137334        1311 0.2512
   Total CN segment #3 ([1.8545e+08,2.47137e+08]) of 3...done
     chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
   1          1     1    1    554484 143926517         7599  1.3859         2111
   2          1     2    1 143926517 185449813         2668  2.0704          774
   3          1     3    1 185449813 247137334         4391  2.6341         1311
     tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean
   1         2111    554484 143926517        2111 0.5237
   2          774 143926517 185449813         774 0.1542
   3         1311 185449813 247137334        1311 0.2512
   Calculating (C1,C2) per segment...
   Calculating (C1,C2) per segment...done
   Number of segments: 3
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Post-segmenting TCNs...
   Number of segments: 3
   Number of chromosomes: 1
   [1] 1
   Chromosome 1 ('chr01') of 1...
    Rows:
    [1] 1 2 3
    Number of segments: 3
    TCN segment #1 ('1') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #1 ('1') of 3...done
    TCN segment #2 ('2') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #2 ('2') of 3...done
    TCN segment #3 ('3') of 3...
     Nothing todo. Only one DH segmentation. Skipping.
    TCN segment #3 ('3') of 3...done
   Chromosome 1 ('chr01') of 1...done
   Update (C1,C2) per segment...
   Update (C1,C2) per segment...done
  Post-segmenting TCNs...done
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111    554484 143926517        2111 0.5237 0.3300521 1.055848
  2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
  3         1311 185449813 247137334        1311 0.2512 0.9862070 1.647893
    chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111    554484 143926517        2111 0.5237 0.3300521 1.055848
  2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
  3         1311 185449813 247137334        1311 0.2512 0.9862070 1.647893
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111        2111 0.5237 0.3300521 1.055848
  2          774         774 0.1542 0.8755722 1.194828
  3         1311        1311 0.2512 0.9862070 1.647893
  > 
  > # Plot results
  > plotTracks(fit)
  > 
  > # Sanity check
  > stopifnot(nbrOfSegments(fit) == nSegs)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Bootstrap segment level estimates
  > # (used by the AB caller, which, if skipped here,
  > #  will do it automatically)
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- bootstrapTCNandDHByRegion(fit, B=B, verbose=-10)
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...
   Already done?
    tcn_2.5%    tcn_5%   tcn_95% tcn_97.5%   dh_2.5%     dh_5%    dh_95%  dh_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
     c1_2.5%     c1_5%    c1_95%  c1_97.5%   c2_2.5%     c2_5%    c2_95%  c2_97.5% 
       FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE 
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...
     Number of loci: 14658
     Number of SNPs: 4196
     Number of non-SNPs: 10462
    Identifying heterozygous & homozygous SNPs and non-polymorphic loci...done
     num [1:3, 1:100, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Segment #1 (chr 1, tcnId=1, dhId=1) of 3...
       chromosome tcnId dhId tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     1          1     1    1   554484 143926517         7599  1.3859         2111
       tcnNbrOfHets dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     1         2111  554484 143926517        2111 0.5237 0.3300521 1.055848
     Number of TCNs: 7599
     Number of DHs: 2111
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
      Non-polymorphic loci:
       int [1:5488] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs to resample for TCN:
       int [1:2111] 10 12 24 28 31 33 34 39 46 48 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:5488] 1 2 3 4 5 6 7 8 9 11 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:7599] 1 2 3 4 5 6 7 8 9 10 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (2111,0,5488)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #1 (chr 1, tcnId=1, dhId=1) of 3...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 3...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     2          1     2    1 143926517 185449813         2668  2.0704          774
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean    c1Mean   c2Mean
     2          774 143926517 185449813         774 0.1542 0.8755722 1.194828
     Number of TCNs: 2668
     Number of DHs: 774
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
      Non-polymorphic loci:
       int [1:1894] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      Heterozygous SNPs to resample for TCN:
       int [1:774] 7614 7616 7626 7627 7628 7635 7638 7639 7640 7642 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:1894] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:2668] 7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (774,0,1894)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #2 (chr 1, tcnId=2, dhId=1) of 3...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 3...
       chromosome tcnId dhId  tcnStart    tcnEnd tcnNbrOfLoci tcnMean tcnNbrOfSNPs
     3          1     3    1 185449813 247137334         4391  2.6341         1311
       tcnNbrOfHets   dhStart     dhEnd dhNbrOfLoci dhMean   c1Mean   c2Mean
     3         1311 185449813 247137334        1311 0.2512 0.986207 1.647893
     Number of TCNs: 4391
     Number of DHs: 1311
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
      int [1:14658] 1 2 3 4 5 6 7 8 9 10 ...
      int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap DH means...
      Heterozygous SNPs to resample for DH:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
     Identify loci used to bootstrap DH means...done
     Identify loci used to bootstrap TCN means...
      SNPs:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Non-polymorphic loci:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs to resample for TCN:
       int [1:1311] 10269 10271 10281 10282 10283 10293 10295 10297 10300 10302 ...
      Homozygous SNPs to resample for TCN:
       int(0) 
      Non-polymorphic loci to resample for TCN:
       int [1:3080] 10268 10270 10272 10273 10274 10275 10276 10277 10278 10279 ...
      Heterozygous SNPs with non-DH to resample for TCN:
       int(0) 
      Loci to resample for TCN:
       int [1:4391] 10268 10269 10270 10271 10272 10273 10274 10275 10276 10277 ...
     Identify loci used to bootstrap TCN means...done
     Number of (#hets, #homs, #nonSNPs): (1311,0,3080)
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...
      Number of bootstrap samples: 100
     Bootstrapping while preserving (#hets, #homs, #nonSNPs)...done
    Segment #3 (chr 1, tcnId=3, dhId=1) of 3...done
    Bootstrapped segment mean levels
     num [1:3, 1:100, 1:4] 1.38 2.08 2.63 1.38 2.07 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : NULL
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...
      num [1:3, 1:100, 1:4] 1.38 2.08 2.63 1.38 2.07 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Calculating (C1,C2) mean levels from (TCN,DH) mean levels...done
    Calculating polar (alpha,radius,manhattan) for change points...
      num [1:2, 1:100, 1:2] -0.5588 -0.0962 -0.5365 -0.1285 -0.5378 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:2] "c1" "c2"
     Bootstrapped change points
      num [1:2, 1:100, 1:5] -2.89 -1.78 -2.87 -1.86 -2.88 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : NULL
       ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Calculating polar (alpha,radius,manhattan) for change points...done
   Bootstrapping (TCN,DH,C1,C2) segment mean levels...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...
     num [1:3, 1:4, 1:4] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Field #1 ('tcn') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #1 ('tcn') of 4...done
    Field #2 ('dh') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #2 ('dh') of 4...done
    Field #3 ('c1') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #3 ('c1') of 4...done
    Field #4 ('c2') of 4...
     Segment #1 of 3...
     Segment #1 of 3...done
     Segment #2 of 3...
     Segment #2 of 3...done
     Segment #3 of 3...
     Segment #3 of 3...done
    Field #4 ('c2') of 4...done
    Bootstrap statistics
     num [1:3, 1:4, 1:4] 1.38 2.06 2.62 1.38 2.06 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
    Statistical sanity checks (iff B >= 100)...
     Available summaries: 2.5%, 5%, 95%, 97.5%
     Available quantiles: 0.025, 0.05, 0.95, 0.975
      num [1:3, 1:4, 1:4] 1.38 2.06 2.62 1.38 2.06 ...
      - attr(*, "dimnames")=List of 3
       ..$ : NULL
       ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
       ..$ : chr [1:4] "tcn" "dh" "c1" "c2"
     Field #1 ('tcn') of 4...
      Seg   1. mean=1.3859, range=[1.38092,1.3949], n=7599
      Seg   2. mean=2.0704, range=[2.05747,2.08326], n=2668
      Seg   3. mean=2.6341, range=[2.62068,2.64694], n=4391
     Field #1 ('tcn') of 4...done
     Field #2 ('dh') of 4...
      Seg   1. mean=0.5237, range=[0.51753,0.532002], n=2111
      Seg   2. mean=0.1542, range=[0.144468,0.16453], n=774
      Seg   3. mean=0.2512, range=[0.242575,0.258832], n=1311
     Field #2 ('dh') of 4...done
     Field #3 ('c1') of 4...
      Seg   1. mean=0.330052, range=[0.323996,0.336038], n=2111
      Seg   2. mean=0.875572, range=[0.86318,0.887699], n=774
      Seg   3. mean=0.986207, range=[0.975123,0.998982], n=1311
     Field #3 ('c1') of 4...done
     Field #4 ('c2') of 4...
      Seg   1. mean=1.05585, range=[1.05006,1.06231], n=2111
      Seg   2. mean=1.19483, range=[1.18417,1.2081], n=774
      Seg   3. mean=1.64789, range=[1.63403,1.66098], n=1311
     Field #4 ('c2') of 4...done
    Statistical sanity checks (iff B >= 100)...done
   Summarizing bootstrapped segment ('tcn', 'dh', 'c1', 'c2') data...done
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...
     num [1:2, 1:4, 1:5] NA NA NA NA NA NA NA NA NA NA ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
    Field #1 ('alpha') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #1 ('alpha') of 5...done
    Field #2 ('radius') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #2 ('radius') of 5...done
    Field #3 ('manhattan') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #3 ('manhattan') of 5...done
    Field #4 ('d1') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #4 ('d1') of 5...done
    Field #5 ('d2') of 5...
     Changepoint #1 of 2...
     Changepoint #1 of 2...done
     Changepoint #2 of 2...
     Changepoint #2 of 2...done
    Field #5 ('d2') of 5...done
    Bootstrap statistics
     num [1:2, 1:4, 1:5] -2.92 -1.86 -2.91 -1.85 -2.87 ...
     - attr(*, "dimnames")=List of 3
      ..$ : NULL
      ..$ : chr [1:4] "2.5%" "5%" "95%" "97.5%"
      ..$ : chr [1:5] "alpha" "radius" "manhattan" "d1" ...
   Summarizing bootstrapped changepoint ('alpha', 'radius', 'manhattan', 'd1', 'd2') data...done
  Resample (TCN,DH) signals and re-estimate summaries for segment & changepoint...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean
  1         2111        2111 0.5237 0.3300521 1.055848
  2          774         774 0.1542 0.8755722 1.194828
  3         1311        1311 0.2512 0.9862070 1.647893
  > plotTracks(fit)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Calling segments in allelic balance (AB) and
  > # in loss-of-heterozygosity (LOH)
  > # NOTE: Ideally, this should be done on whole-genome data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- callAB(fit, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in DH): 0.3466649145302
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called allelic balance (AB): 2 (66.67%) of 3
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > fit <- callLOH(fit, verbose=-10)
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...
   delta (offset adjusting for bias in C1): 0.771236438183453
   alpha (CI quantile; significance level): 0.05
   Calling segments...
    Number of segments called low C1 (LowC1, "LOH_C1"): 1 (33.33%) of 3
   Calling segments...done
  Calling segments of allelic balance from one-sided DH bootstrap confidence intervals...done
  > print(fit)
    chromosome tcnId dhId     start       end tcnNbrOfLoci tcnMean tcnNbrOfSNPs
  1          1     1    1    554484 143926517         7599  1.3859         2111
  2          1     2    1 143926517 185449813         2668  2.0704          774
  3          1     3    1 185449813 247137334         4391  2.6341         1311
    tcnNbrOfHets dhNbrOfLoci dhMean    c1Mean   c2Mean abCall lohCall
  1         2111        2111 0.5237 0.3300521 1.055848  FALSE    TRUE
  2          774         774 0.1542 0.8755722 1.194828   TRUE   FALSE
  3         1311        1311 0.2512 0.9862070 1.647893   TRUE   FALSE
  > plotTracks(fit)
  > 
  > proc.time()
     user  system elapsed 
     3.03    0.37    3.85 
Running the tests in 'tests/segmentByPairedPSCBS,report.R' failed.
Complete output:
  > # This test script calls a report generator which requires
  > # the 'ggplot2' package, which in turn will require packages
  > # 'colorspace', 'dichromat', 'munsell', 'reshape2' and 'scales'.
  > 
  > # Only run this test in full testing mode
  > if (Sys.getenv("_R_CHECK_FULL_") != "") {
  + library("PSCBS")
  + 
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # Load SNP microarray data
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + data <- PSCBS::exampleData("paired.chr01")
  + str(data)
  + 
  + 
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # Paired PSCBS segmentation
  + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  + # Drop single-locus outliers
  + dataS <- dropSegmentationOutliers(data)
  + 
  + # Speed up example by segmenting fewer loci
  + dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  + 
  + str(dataS)
  + 
  + gaps <- findLargeGaps(dataS, minLength=2e6)
  + knownSegments <- gapsToSegments(gaps)
  + 
  + # Paired PSCBS segmentation
  + fit <- segmentByPairedPSCBS(dataS, knownSegments=knownSegments,
  +                             seed=0xBEEF, verbose=-10)
  + 
  + # Fake a multi-chromosome segmentation
  + fit1 <- fit
  + fit2 <- renameChromosomes(fit, from=1, to=2)
  + fit <- c(fit1, fit2)
  + 
  + report(fit, sampleName="PairedPSCBS", studyName="PSCBS-Ex", verbose=-10)
  + 
  + } # if (Sys.getenv("_R_CHECK_FULL_"))
  > 
  > proc.time()
     user  system elapsed 
     0.14    0.07    0.21