Running ‘PairedPSCBS,boot.R’ [1s/2s]
  Running ‘findLargeGaps.R’ [1s/1s]
  Running ‘randomSeed.R’ [1s/1s]
  Running ‘segmentByCBS,calls.R’ [0s/1s]
  Running ‘segmentByCBS,futures.R’ [2s/3s]
  Running ‘segmentByCBS,median.R’ [2s/3s]
  Running ‘segmentByCBS,prune.R’ [2s/3s]
  Running ‘segmentByCBS,report.R’ [0s/1s]
  Running ‘segmentByCBS,shiftTCN.R’ [1s/3s]
  Running ‘segmentByCBS,weights.R’ [1s/2s]
  Running ‘segmentByCBS.R’ [1s/2s]
  Running ‘segmentByNonPairedPSCBS,medianDH.R’ [5s/8s]
  Running ‘segmentByPairedPSCBS,DH.R’ [6s/9s]
  Running ‘segmentByPairedPSCBS,calls.R’ [1s/2s]
  Running ‘segmentByPairedPSCBS,futures.R’ [8s/11s]
  Running ‘segmentByPairedPSCBS,noNormalBAFs.R’ [6s/9s]
  Running ‘segmentByPairedPSCBS,report.R’ [0s/1s]
  Running ‘segmentByPairedPSCBS,seqOfSegmentsByDP.R’ [1s/2s]
  Running ‘segmentByPairedPSCBS.R’ [1s/2s]
Running the tests in ‘tests/PairedPSCBS,boot.R’ failed.
Complete output:
  > ###########################################################
  > # This tests:
  > # - Bootstrapping for PairedPSCBS objects
  > ###########################################################
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Load SNP microarray data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > data <- PSCBS::exampleData("paired.chr01")
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Paired PSCBS segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Drop single-locus outliers
  > dataS <- dropSegmentationOutliers(data)
  > dataS <- dataS[seq(from=1, to=nrow(data), by=5),]
  > nSegs <- 4L
  > 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 ...
  > # Segment known regions
  > knownSegments <- data.frame(
  +   chromosome = c(        1,  1,         1),
  +   start      = c(     -Inf, NA, 141510003),
  +   end        = c(120992603, NA,      +Inf)
  + )
  > fit <- segmentByPairedPSCBS(dataS, knownSegments=knownSegments, avgDH="median", seed=0xBEEF)
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByPairedPSCBS ... segmentByPairedPSCBS.default -> segmentByCBS -> segmentByCBS.default
  Execution halted
Running the tests in ‘tests/segmentByCBS,futures.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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", "multiprocess")
  > 
  > ## Test 'future.BatchJobs' futures?
  > pkg <- "future.BatchJobs"
  > if (require(pkg, character.only=TRUE)) {
  +   strategies <- c(strategies, "batchjobs_local")
  + }
  Loading required package: future.BatchJobs
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'future.BatchJobs'
  > 
  > message("Future strategies to test: ", paste(sQuote(strategies), collapse=", "))
  Future strategies to test: 'sequential', 'multiprocess'
  > 
  > 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(407, 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(407, 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(407, -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 'multiprocess' 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...
     Segmenting by CBS...
      Chromosome: 1
    Chromosome #2 ('Chr02') of 3...done
    Chromosome #3 ('Chr03') of 3...
     Segmenting by CBS...
      Chromosome: 2
       Random seed temporarily set (seed=c(407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
       Random seed temporarily set (seed=c(407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
     Segmenting by CBS...done
     Segmenting by CBS...done
    Chromosome #3 ('Chr03') of 3...done
     Segmenting by CBS...
      Chromosome: 3
       Random seed temporarily set (seed=c(407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
     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
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByCBS ... segmentByCBS.data.frame -> segmentByCBS -> segmentByCBS.default
  Segmenting by CBS...done
  Execution halted
Running the tests in ‘tests/segmentByCBS,shiftTCN.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 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)
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByCBS ... eval -> eval -> value -> value.Future -> resignalCondition
  Execution halted
Running the tests in ‘tests/segmentByCBS,weights.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByCBS ... segmentByCBS.data.frame -> segmentByCBS -> segmentByCBS.default
  Segmenting by CBS...done
  Execution halted
Running the tests in ‘tests/segmentByCBS.R’ failed.
Complete output:
  > ###########################################################
  > # This tests:
  > # - segmentByCBS(...)
  > # - segmentByCBS(..., knownSegments)
  > # - tileChromosomes()
  > # - plotTracks()
  > ###########################################################
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > subplots <- R.utils::subplots
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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
  > 
  > 
  > subplots(8, ncol=1L)
  > par(mar=c(1.7,1,0.2,1)+0.1)
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segmentation
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > fit <- segmentByCBS(y, x=x)
  > sampleName(fit) <- "CBS_Example"
  > print(fit)
     sampleName chromosome       start      end nbrOfLoci    mean
  1 CBS_Example          0    55167.82 20774251       201  0.0164
  2 CBS_Example          0 20774250.85 29320105        99  1.0474
  3 CBS_Example          0 29320104.86 65874675       298 -0.0203
  4 CBS_Example          0 65874675.06 81348129       151 -1.0813
  5 CBS_Example          0 81348129.20 99910827       200 -0.0612
  > 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.
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segmentation with some known change points
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > knownSegments <- data.frame(
  +   chromosome=c(    0,   0),
  +   start     =x[c(  1, 401)],
  +   end       =x[c(349,   J)]
  + )
  > fit2 <- segmentByCBS(y, x=x, knownSegments=knownSegments, verbose=TRUE)
  Segmenting by CBS...
   Chromosome: 0
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByCBS -> segmentByCBS.default
  Segmenting by CBS...done
  Execution halted
Running the tests in ‘tests/segmentByPairedPSCBS,calls.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.552
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.552
      .. .. ..- 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  -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  0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. .. .. ..$ density: num  0.522 0.552
      .. .. .. ..- attr(*, "class")= chr  "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  0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. ..$ density: num  0.522 0.552
      .. ..- attr(*, "class")= chr  "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
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByPairedPSCBS ... segmentByPairedPSCBS.default -> segmentByCBS -> segmentByCBS.default
     Segmenting multiple segments on current chromosome...done
    Segmenting by CBS...done
   Identification of change points by total copy numbers...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Execution halted
Running the tests in ‘tests/segmentByPairedPSCBS,futures.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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", "multiprocess")
  > 
  > ## Test 'future.BatchJobs' futures?
  > pkg <- "future.BatchJobs"
  > if (require(pkg, character.only=TRUE)) {
  +   strategies <- c(strategies, "batchjobs_local")
  + }
  Loading required package: future.BatchJobs
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'future.BatchJobs'
  > 
  > message("Future strategies to test: ", paste(sQuote(strategies), collapse=", "))
  Future strategies to test: 'sequential', 'multiprocess'
  > 
  > 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
  +   stopifnot(all.equal(fit, 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. ..$ density: num [1:5] 1.623 0.465 1.126 0.497 1.588
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. ..$ density: num [1:2] 0.465 0.497
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. ..$ density: num [1:5] 1.623 0.465 1.126 0.497 1.588
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. ..$ density: num [1:2] 0.465 0.497
      .. .. ..- 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  -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  0.312 0.678
      .. .. .. .. ..$ n                  : int 43920
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. .. ..$ density: num  1.623 0.465 1.126 0.497 1.588
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.312 0.678
      .. .. .. .. .. ..$ density: num  0.465 0.497
      .. .. .. ..- attr(*, "class")= chr  "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  0.312 0.678
      .. .. ..$ n                  : int 43920
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. ..$ density: num  1.623 0.465 1.126 0.497 1.588
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.312 0.678
      .. .. .. ..$ density: num  0.465 0.497
      .. ..- attr(*, "class")= chr  "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(407, 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(407, 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] 407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.407 0 0.419 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.032 0 0.037 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.011 0 0.011 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.017 0 0.032 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, 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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.436 0 0.611 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.03 0 0.029 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -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 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.017 0 0.017 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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(407, -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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.421 0.004 0.485 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.026 0.004 0.03 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.009 0 0.011 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.018 0 0.018 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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] 407 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 'multiprocess' 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. ..$ density: num [1:5] 1.623 0.465 1.126 0.497 1.588
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. ..$ density: num [1:2] 0.465 0.497
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. ..$ density: num [1:5] 1.623 0.465 1.126 0.497 1.588
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.312 0.678
      .. .. .. .. ..$ density: num [1:2] 0.465 0.497
      .. .. ..- 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  -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  0.312 0.678
      .. .. .. .. ..$ n                  : int 43920
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. .. .. ..$ density: num  1.623 0.465 1.126 0.497 1.588
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.312 0.678
      .. .. .. .. .. ..$ density: num  0.465 0.497
      .. .. .. ..- attr(*, "class")= chr  "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  0.312 0.678
      .. .. ..$ n                  : int 43920
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.0999 0.312 0.4986 0.6775 0.8922
      .. .. .. ..$ density: num  1.623 0.465 1.126 0.497 1.588
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.312 0.678
      .. .. .. ..$ density: num  0.465 0.497
      .. ..- attr(*, "class")= chr  "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)
     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...
    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)
       '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
     Segmenting paired tumor-normal signals using Paired PSCBS...
      Setup up data...
      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
       '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 ...
      Random seed temporarily set (seed=c(407, 1066287653, -51199871, 161854402, -1995183193, 1503453565, -747102133), kind="L'Ecuyer-CMRG")
      Produced 2 seeds from this stream for future usage
      Setup up data...done
      Identification of change points by total copy numbers...
      Ordering data along genome...
       Segmenting by CBS...
       '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 ...
        Chromosome: 1
      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
         Random seed temporarily set (seed=c(407, 1797822437, 388243314, 91406689, -519578635, -1381924756, 1089253019), kind="L'Ecuyer-CMRG")
       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(407, 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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.473 0 0.568 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.033 0 0.032 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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 ...
       Segmenting by CBS...done
        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
       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] 407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.448 0.005 0.76 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 1797822437 388243314 91406689 -519578635 -1381924756 1089253019
      Total CN segment #2 ([1.43927e+08,1.8545e+08]) of 3...
       Number of TCN loci in segment: 2668
       Locus data for TCN segment:
      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
       '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 ...
       Locus data for TCN segment:
       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
       '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...
          Random seed temporarily set (seed=c(407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        Segmenting by CBS...
         Chromosome: 1
          Random seed temporarily set (seed=c(407, -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] 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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.01 0 0.011 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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...
        Segmenting by CBS...done
         Chromosome: 2
          Random seed temporarily set (seed=c(407, -1924040949, -1632234809, -437763632, -1464377300, 676654412, 2080370711), kind="L'Ecuyer-CMRG")
        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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.032 0 0.06 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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
        Segmenting by CBS...done
       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...
        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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.018 0 0.019 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        DH segmentation (locally-indexed) rows:
          startRow endRow
        1        2   4388
         Chromosome: 1
         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
          Random seed temporarily set (seed=c(407, -821273412, -52578226, 1415511586, 721384351, -665928286, 1316562960), kind="L'Ecuyer-CMRG")
       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':	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] 407 -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 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -821273412 -52578226 1415511586 721384351 -665928286 1316562960
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.018 0 0.018 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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 #3 ('Chr03') of 3...done
    Merging (independently) segmented chromosome...
     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(407, -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(407, -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] 407 -1924040949 -1632234809 -437763632 -1464377300 676654412 2080370711
        - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
        - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.449 0.003 0.916 0 0
         ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
        - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
        - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.031 0 0.049 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.011 0 0.011 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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(407, -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] 407 -1371615447 -889757879 1692656974 -1723952224 1378814990 1816467252
         - attr(*, "class")= chr [1:2] "CBS" "AbstractCBS"
         - attr(*, "processingTime")= 'proc_time' Named num [1:5] 0.019 0 0.027 0 0
          ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
         - attr(*, "pkgDetails")= chr "DNAcopy v1.55.0"
         - attr(*, "randomSeed")= int [1:7] 407 -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] 407 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("- segmentByCBS() w/ known segments using '%s' futures ...", strategy))
  +   plan(strategy)
  +   fit <- segmentByPairedPSCBS(dataT, knownSegments=knownSegments, seed=0xBEEF, verbose=TRUE)
  +   fits[[strategy]] <- fit
  +   stopifnot(all.equal(fit, fits[[1]]))
  + }
  - segmentByCBS() 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.552
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.552
      .. .. ..- 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  -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  0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. .. .. ..$ density: num  0.522 0.552
      .. .. .. ..- attr(*, "class")= chr  "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  0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. ..$ density: num  0.522 0.552
      .. ..- attr(*, "class")= chr  "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
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByPairedPSCBS ... segmentByPairedPSCBS.default -> segmentByCBS -> segmentByCBS.default
    Segmenting by CBS...done
   Identification of change points by total copy numbers...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Execution halted
Running the tests in ‘tests/segmentByPairedPSCBS,seqOfSegmentsByDP.R’ failed.
Complete output:
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > subplots <- R.utils::subplots
  > stext <- R.utils::stext
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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)
  > 
  > # 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 <- 12L
  +   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)
  > 
  > 
  > gaps <- findLargeGaps(dataS, minLength=2e6)
  > knownSegments <- gapsToSegments(gaps, dropGaps=TRUE)
  > 
  > # 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.552
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.552
      .. .. ..- 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  -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  0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. .. .. ..$ density: num  0.522 0.552
      .. .. .. ..- attr(*, "class")= chr  "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  0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. ..$ density: num  0.522 0.552
      .. ..- attr(*, "class")= chr  "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 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
     Segmenting multiple segments on current chromosome...
      Number of segments: 2
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByPairedPSCBS ... segmentByPairedPSCBS.default -> segmentByCBS -> segmentByCBS.default
     Segmenting multiple segments on current chromosome...done
    Segmenting by CBS...done
   Identification of change points by total copy numbers...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Execution halted
Running the tests in ‘tests/segmentByPairedPSCBS.R’ failed.
Complete output:
  > ###########################################################
  > # This tests:
  > # - segmentByPairedPSCBS(...)
  > # - segmentByPairedPSCBS(..., knownSegments)
  > # - tileChromosomes()
  > # - plotTracks()
  > ###########################################################
  > library("PSCBS")
  PSCBS v0.64.0 successfully loaded. See ?PSCBS for help.
  
  Attaching package: 'PSCBS'
  
  The following objects are masked from 'package:base':
  
      append, load
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # 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 ...
  > 
  > fig <- 1
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # (a) Don't segment the centromere (and force a separator)
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > knownSegments <- data.frame(
  +   chromosome = c(        1,  1,         1),
  +   start      = c(     -Inf, NA, 141510003),
  +   end        = c(120992603, NA,      +Inf)
  + )
  > 
  > 
  > # 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. ..$ density: num [1:2] 0.522 0.552
      ..- 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   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. ..$ x      : num [1:5] 0.104 0.315 0.499 0.677 0.885
      .. .. .. .. ..$ density: num [1:5] 1.48 0.522 1.056 0.552 1.454
      .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. ..$ x      : num [1:2] 0.315 0.677
      .. .. .. .. ..$ density: num [1:2] 0.522 0.552
      .. .. ..- 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  -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  0.315 0.677
      .. .. .. .. ..$ n                  : int 14640
      .. .. .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. .. .. ..$ density: num  0.522 0.552
      .. .. .. ..- attr(*, "class")= chr  "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  0.315 0.677
      .. .. ..$ n                  : int 14640
      .. .. ..$ fit                :Classes 'PeaksAndValleys' and 'data.frame':	5 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 1 2 1 2 1
      .. .. .. ..$ x      : num  0.104 0.315 0.499 0.677 0.885
      .. .. .. ..$ density: num  1.48 0.522 1.056 0.552 1.454
      .. .. ..$ fitValleys         :Classes 'PeaksAndValleys' and 'data.frame':	2 obs. of  3 variables:
      .. .. .. ..$ type   : Factor w/ 2 levels "peak","valley": 2 2
      .. .. .. ..$ x      : num  0.315 0.677
      .. .. .. ..$ density: num  0.522 0.552
      .. ..- attr(*, "class")= chr  "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
    1          1      -Inf 120992603
    2          1        NA        NA
    3          1 141510003       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
  Error in `[<-.data.frame`(`*tmp*`, !isSplitter, 1, value = 1L) : 
    replacement has 1 row, data has 0
  Calls: segmentByPairedPSCBS ... segmentByPairedPSCBS.default -> segmentByCBS -> segmentByCBS.default
     Segmenting multiple segments on current chromosome...done
    Segmenting by CBS...done
   Identification of change points by total copy numbers...done
  Segmenting paired tumor-normal signals using Paired PSCBS...done
  Execution halted