Running ‘testthat.R’ [8s/12s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(aLFQ)
  > 
  > 
  > test_check("aLFQ")
  ── 1. Error: AbsoluteQuantification.default (@test_AbsoluteQuantification.R#6)  
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE))[c("is_calibrated", 
         "mfe", "r.squared", "calibration_covar")], equals(list(is_calibrated = TRUE, 
         mfe = 1.670342, r.squared = 0.9512209, calibration_covar = 16.9878), tolerance = 0.001)) at testthat/test_AbsoluteQuantification.R:6
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE))
  7: setdiff(c("run_id", "protein_id", "concentration", "response"), names(data))
  8: as.vector(y)
  9: ProteinInference.default(UPS2_SRM, peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", 
         peptide_summary = "mean", transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", combine_precursors = FALSE, consensus_proteins = TRUE, 
         consensus_peptides = TRUE, consensus_transitions = TRUE)
  10: PeptideInference(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  11: PeptideInference.default(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  12: data.dt[data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]]
  13: `[.data.table`(data.dt, data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)])
  14: eval(.massagei(isub), x, parent.frame())
  15: eval(.massagei(isub), x, parent.frame())
  16: data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]
  17: `[.data.table`(data.dt, , list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt))
  
  ── 2. Error: predict.AbsoluteQuantification (@test_AbsoluteQuantification.R#13) 
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(predict.AbsoluteQuantification(AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, 
         peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", 
         transition_topx = 3, transition_strictness = "loose", transition_summary = "sum", 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)))$prediction$concentration[1:5], equals(c(0.29041894, 
         4.77261553, 1.57177383, 5.84919886, 5.4661825), tolerance = 0.001)) at testthat/test_AbsoluteQuantification.R:13
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: predict.AbsoluteQuantification(AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, 
         peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", 
         transition_topx = 3, transition_strictness = "loose", transition_summary = "sum", 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)))
  7: AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE))
  8: setdiff(c("run_id", "protein_id", "concentration", "response"), names(data))
  9: as.vector(y)
  10: ProteinInference.default(UPS2_SRM, peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", 
         peptide_summary = "mean", transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", combine_precursors = FALSE, consensus_proteins = TRUE, 
         consensus_peptides = TRUE, consensus_transitions = TRUE)
  11: PeptideInference(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  12: PeptideInference.default(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  13: data.dt[data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]]
  14: `[.data.table`(data.dt, data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)])
  15: eval(.massagei(isub), x, parent.frame())
  16: eval(.massagei(isub), x, parent.frame())
  17: data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]
  18: `[.data.table`(data.dt, , list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt))
  
  ── 3. Error: cval.AbsoluteQuantification: Monte Carlo (@test_AbsoluteQuantificat
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(cval.AbsoluteQuantification(AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, 
         peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", 
         transition_topx = 3, transition_strictness = "loose", transition_summary = "sum", 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)), cval_method = "mc", mcx = 2)$cval[c("r.squared", 
         "mfe")], equals(list(r.squared = 0.9292566, mfe = 1.761073), tolerance = 0.001)) at testthat/test_AbsoluteQuantification.R:26
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: cval.AbsoluteQuantification(AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, 
         peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", 
         transition_topx = 3, transition_strictness = "loose", transition_summary = "sum", 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)), cval_method = "mc", mcx = 2)
  7: AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE))
  8: setdiff(c("run_id", "protein_id", "concentration", "response"), names(data))
  9: as.vector(y)
  10: ProteinInference.default(UPS2_SRM, peptide_method = "top", peptide_topx = 1, peptide_strictness = "loose", 
         peptide_summary = "mean", transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", combine_precursors = FALSE, consensus_proteins = TRUE, 
         consensus_peptides = TRUE, consensus_transitions = TRUE)
  11: PeptideInference(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  12: PeptideInference.default(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  13: data.dt[data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]]
  14: `[.data.table`(data.dt, data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)])
  15: eval(.massagei(isub), x, parent.frame())
  16: eval(.massagei(isub), x, parent.frame())
  17: data.dt[, list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt)]
  18: `[.data.table`(data.dt, , list(min_mean_transition_intensity = min(strictnessfilter.ProteinInference(sort(mean_transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness)), 
         na.rm = T), by = key(data.dt))
  
  ── 4. Error: AbsoluteQuantification.default (@test_AbsoluteQuantification.R#52) 
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: AbsoluteQuantification.default(ProteinInference.default(UPS2_SRM_REP, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE)) at testthat/test_AbsoluteQuantification.R:52
  2: setdiff(c("run_id", "protein_id", "concentration", "response"), names(data))
  3: as.vector(y)
  4: ProteinInference.default(UPS2_SRM_REP, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", combine_precursors = FALSE, 
         consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE)
  5: PeptideInference(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  6: PeptideInference.default(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  7: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 5. Error: ProteinInference.default: Absolute abundance estimation of endogeno
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(ProteinInference.default(aqua_data_single_peptide, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)$concentration, equals(c("12.5", "?", "25.3333333333333", 
         "?"))) at testthat/test_ProteinInference.R:9
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: ProteinInference.default(aqua_data_single_peptide, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, 
         consensus_transitions = TRUE)
  7: conc_peptides[, list(concentration = (peptide_intensity[which(concentration == "?")]/peptide_intensity[which(concentration != 
         "?")]) * as.numeric(concentration[which(concentration != "?")])), by = key(conc_peptides)]
  8: `[.data.table`(conc_peptides, , list(concentration = (peptide_intensity[which(concentration == 
         "?")]/peptide_intensity[which(concentration != "?")]) * as.numeric(concentration[which(concentration != 
         "?")])), by = key(conc_peptides))
  
  ── 6. Error: ProteinInference.default: More complex data (@test_ProteinInference
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(ProteinInference.default(UPS2_SRM_REP, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptide = TRUE, 
         consensus_transitions = TRUE), equals(structure(list(run_id = c("UPS2_SRM_REP1", 
         "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", 
         "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", 
         "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", 
         "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", 
         "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", "UPS2_SRM_REP1", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", "UPS2_SRM_REP2", 
         "UPS2_SRM_REP2", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", 
         "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", 
         "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", 
         "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", 
         "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", "UPS2_SRM_REP3", 
         "UPS2_SRM_REP3", "UPS2_SRM_REP3"), protein_id = c("O76070ups|SYUG_HUMAN_UPS", 
         "P00167ups|CYB5_HUMAN_UPS", "P00709ups|LALBA_HUMAN_UPS", "P00915ups|CAH1_HUMAN_UPS", 
         "P00918ups|CAH2_HUMAN_UPS", "P01008ups|ANT3_HUMAN_UPS", "P01127ups|PDGFB_HUMAN_UPS", 
         "P01133ups|EGF_HUMAN_UPS", "P01344ups|IGF2_HUMAN_UPS", "P02144ups|MYG_HUMAN_UPS", 
         "P02753ups|RETBP_HUMAN_UPS", "P02768ups|ALBU_HUMAN_UPS", "P04040ups|CATA_HUMAN_UPS", 
         "P06732ups|KCRM_HUMAN_UPS", "P12081ups|SYHC_HUMAN_UPS", "P15559ups|NQO1_HUMAN_UPS", 
         "P16083ups|NQO2_HUMAN_UPS", "P41159ups|LEP_HUMAN_UPS", "P61626ups|LYSC_HUMAN_UPS", 
         "P61769ups|B2MG_HUMAN_UPS", "P62937ups|PPIA_HUMAN_UPS", "P62988ups|UBIQ_HUMAN_UPS", 
         "P63165ups|SUMO1_HUMAN_UPS", "P68871ups|HBB_HUMAN_UPS", "P69905ups|HBA_HUMAN_UPS", 
         "Q06830ups|PRDX1_HUMAN_UPS", "O76070ups|SYUG_HUMAN_UPS", "P00167ups|CYB5_HUMAN_UPS", 
         "P00709ups|LALBA_HUMAN_UPS", "P00915ups|CAH1_HUMAN_UPS", "P00918ups|CAH2_HUMAN_UPS", 
         "P01008ups|ANT3_HUMAN_UPS", "P01127ups|PDGFB_HUMAN_UPS", "P01133ups|EGF_HUMAN_UPS", 
         "P01344ups|IGF2_HUMAN_UPS", "P02144ups|MYG_HUMAN_UPS", "P02753ups|RETBP_HUMAN_UPS", 
         "P02768ups|ALBU_HUMAN_UPS", "P04040ups|CATA_HUMAN_UPS", "P06732ups|KCRM_HUMAN_UPS", 
         "P12081ups|SYHC_HUMAN_UPS", "P15559ups|NQO1_HUMAN_UPS", "P16083ups|NQO2_HUMAN_UPS", 
         "P41159ups|LEP_HUMAN_UPS", "P61626ups|LYSC_HUMAN_UPS", "P61769ups|B2MG_HUMAN_UPS", 
         "P62937ups|PPIA_HUMAN_UPS", "P62988ups|UBIQ_HUMAN_UPS", "P63165ups|SUMO1_HUMAN_UPS", 
         "P68871ups|HBB_HUMAN_UPS", "P69905ups|HBA_HUMAN_UPS", "Q06830ups|PRDX1_HUMAN_UPS", 
         "O76070ups|SYUG_HUMAN_UPS", "P00167ups|CYB5_HUMAN_UPS", "P00709ups|LALBA_HUMAN_UPS", 
         "P00915ups|CAH1_HUMAN_UPS", "P00918ups|CAH2_HUMAN_UPS", "P01008ups|ANT3_HUMAN_UPS", 
         "P01127ups|PDGFB_HUMAN_UPS", "P01133ups|EGF_HUMAN_UPS", "P01344ups|IGF2_HUMAN_UPS", 
         "P02144ups|MYG_HUMAN_UPS", "P02753ups|RETBP_HUMAN_UPS", "P02768ups|ALBU_HUMAN_UPS", 
         "P04040ups|CATA_HUMAN_UPS", "P06732ups|KCRM_HUMAN_UPS", "P12081ups|SYHC_HUMAN_UPS", 
         "P15559ups|NQO1_HUMAN_UPS", "P16083ups|NQO2_HUMAN_UPS", "P41159ups|LEP_HUMAN_UPS", 
         "P61626ups|LYSC_HUMAN_UPS", "P61769ups|B2MG_HUMAN_UPS", "P62937ups|PPIA_HUMAN_UPS", 
         "P62988ups|UBIQ_HUMAN_UPS", "P63165ups|SUMO1_HUMAN_UPS", "P68871ups|HBB_HUMAN_UPS", 
         "P69905ups|HBA_HUMAN_UPS", "Q06830ups|PRDX1_HUMAN_UPS"), concentration = c("0.449", 
         "44.9", "4.49", "?", "?", "?", "0.449", "44.9", "0.449", "?", "?", "?", "44.9", 
         "?", "4.49", "44.9", "4.49", "449", "4.49", "0.449", "44.9", "449", "?", "?", 
         "449", "?", "0.449", "44.9", "4.49", "?", "449", "0.449", "?", "44.9", "0.449", 
         "?", "4.49", "449", "?", "4.49", "?", "?", "?", "449", "4.49", "0.449", "?", 
         "449", "?", "449", "?", "44.9", "0.449", "?", "?", "449", "449", "0.449", "?", 
         "44.9", "0.449", "44.9", "?", "?", "44.9", "4.49", "?", "?", "4.49", "?", "?", 
         "0.449", "44.9", "449", "44.9", "449", "?", "44.9"), response = c(161978.666666667, 
         13113548.6666667, 568833.666666667, 37676253.3333333, 25882206.3333333, 74258.3333333333, 
         46324, 3684505, 89774, 9086594.33333333, 645838.666666667, 73089941.6666667, 
         3486194.5, 549538, 507207, 2309451.5, 589519.666666667, 41890138.6666667, 256876, 
         28007, 11569710.6666667, 26079521.3333333, 4224907.33333333, 24090440.6666667, 
         17080367.3333333, 3852184.66666667, 161978.666666667, 13113548.6666667, 568833.666666667, 
         37676253.3333333, 25882206.3333333, 74258.3333333333, 46324, 3684505, 89774, 
         9086594.33333333, 645838.666666667, 73089941.6666667, 3486194.5, 549538, 507207, 
         2309451.5, 589519.666666667, 41890138.6666667, 256876, 28007, 11569710.6666667, 
         26079521.3333333, 4224907.33333333, 24090440.6666667, 17080367.3333333, 3852184.66666667, 
         161978.666666667, 13113548.6666667, 568833.666666667, 37676253.3333333, 25882206.3333333, 
         74258.3333333333, 46324, 3684505, 89774, 9086594.33333333, 645838.666666667, 
         73089941.6666667, 3486194.5, 549538, 507207, 2309451.5, 589519.666666667, 41890138.6666667, 
         256876, 28007, 11569710.6666667, 26079521.3333333, 4224907.33333333, 24090440.6666667, 
         17080367.3333333, 3852184.66666667)), .Names = c("run_id", "protein_id", "concentration", 
         "response"), row.names = c(NA, -78L), class = "data.frame"))) at testthat/test_ProteinInference.R:35
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: ProteinInference.default(UPS2_SRM_REP, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", transition_topx = 3, 
         transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_proteins = TRUE, consensus_peptide = TRUE, 
         consensus_transitions = TRUE)
  7: PeptideInference(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  8: PeptideInference.default(data, transition_topx = transition_topx, transition_strictness = transition_strictness, 
         transition_summary = transition_summary, consensus_proteins = consensus_proteins, 
         consensus_transitions = consensus_transitions)
  9: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  10: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 7. Error: protein_inference.ProteinInference: Consensus peptide selection (@t
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "top", 
         peptide_topx = 3, peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = TRUE)$response, 
         equals(c(80, 50, 95, 45))) at testthat/test_ProteinInference.R:57
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "top", peptide_topx = 3, 
         peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = TRUE)
  7: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 8. Error: protein_inference.ProteinInference: Combine precursors (@test_Prote
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = FALSE)$response, 
         equals(c(100, 60, 95, 55))) at testthat/test_ProteinInference.R:66
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = FALSE)
  7: data.dt[, list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt))
  
  ── 9. Error: protein_inference.ProteinInference: Summarization of peptides (@tes
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "top", 
         peptide_topx = 2, peptide_strictness = "loose", peptide_summary = "sum", fasta = NA, 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = FALSE)$response, 
         equals(c(180, 100, 170, 90))) at testthat/test_ProteinInference.R:71
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "top", peptide_topx = 2, 
         peptide_strictness = "loose", peptide_summary = "sum", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = FALSE)
  7: data.dt[, list(response = sum(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = sum(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt))
  
  ── 10. Error: protein_inference.ProteinInference: topx selection criterion (@tes
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "top", 
         peptide_topx = 1, peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = FALSE)$response, 
         equals(c(100, 60, 95, 55))) at testthat/test_ProteinInference.R:77
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "top", peptide_topx = 1, 
         peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = FALSE)
  7: data.dt[, list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt))
  
  ── 11. Error: protein_inference.ProteinInference: loose / strict selection crite
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "top", 
         peptide_topx = 3, peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = FALSE)$response, 
         equals(c(90, 50, 85, 45))) at testthat/test_ProteinInference.R:83
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "top", peptide_topx = 3, 
         peptide_strictness = "loose", peptide_summary = "mean", fasta = NA, apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = FALSE)
  7: data.dt[, list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = mean(strictnessfilter.ProteinInference(sort(peptide_intensity, 
         decreasing = TRUE)[1:peptide_topx], strictness = peptide_strictness))), by = key(data.dt))
  
  ── 12. Error: protein_inference.ProteinInference: all peptide_method (@test_Prot
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "all", 
         peptide_summary = "median", fasta = NA, apex_model = NA, combine_precursors = FALSE, 
         consensus_peptides = FALSE)$response, equals(c(90, 50, 85, 45))) at testthat/test_ProteinInference.R:89
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "all", peptide_summary = "median", 
         fasta = NA, apex_model = NA, combine_precursors = FALSE, consensus_peptides = FALSE)
  7: data.dt[, list(response = as.numeric(median(peptide_intensity))), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = as.numeric(median(peptide_intensity))), 
         by = key(data.dt))
  
  ── 13. Error: protein_inference.ProteinInference: iBAQ peptide_method (@test_Pro
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(data.table(UPS2_LFQ), peptide_method = "iBAQ", 
         peptide_summary = "sum", fasta = system.file("extdata", "UPS2.fasta", package = "aLFQ"), 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = TRUE)$response, 
         equals(c(21962200.4166667, 118434366.571429, 69695738.6458333, 77898728, 13265416.6666667, 
             5239841.52777778, 29905.8518518519, 121466692, 8857917.53125, 222967.866666667, 
             137296.205882353, 1644366.41025641, 431313.111111111, 122004965.166667, 980991.333333333, 
             7794690, 46847015, 4293223.45833333, 67804931.8787879, 31584084.1666667, 
             15493374.7619048))) at testthat/test_ProteinInference.R:99
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(data.table(UPS2_LFQ), peptide_method = "iBAQ", 
         peptide_summary = "sum", fasta = system.file("extdata", "UPS2.fasta", package = "aLFQ"), 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = TRUE)
  7: data.dt[, list(response = sum(peptide_intensity)/length(unlist((lapply(as.list(peptide_sequences.fasta[[protein_id[1]]]), 
         function(X) {
             if (nchar(X) >= 6 && nchar(X) <= 30) {
                 return(X)
             } else {
                 return(NA)
             }
         })))[!is.na(unlist((lapply(as.list(peptide_sequences.fasta[[protein_id[1]]]), 
         function(X) {
             if (nchar(X) >= 6 && nchar(X) <= 30) {
                 return(X)
             } else {
                 return(NA)
             }
         }))))])), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(response = sum(peptide_intensity)/length(unlist((lapply(as.list(peptide_sequences.fasta[[protein_id[1]]]), 
         function(X) {
             if (nchar(X) >= 6 && nchar(X) <= 30) {
                 return(X)
             } else {
                 return(NA)
             }
         })))[!is.na(unlist((lapply(as.list(peptide_sequences.fasta[[protein_id[1]]]), 
         function(X) {
             if (nchar(X) >= 6 && nchar(X) <= 30) {
                 return(X)
             } else {
                 return(NA)
             }
         }))))])), by = key(data.dt))
  
  ── 14. Error: protein_inference.ProteinInference: APEX peptide_method (@test_Pro
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(dim(protein_inference.ProteinInference(peptide_data, peptide_method = "APEX", 
         peptide_summary = "sum", fasta = system.file("extdata", "example.fasta", package = "aLFQ"), 
         apex_model = APEX_ORBI.apex, combine_precursors = FALSE, consensus_peptides = TRUE))[2], 
         equals(4)) at testthat/test_ProteinInference.R:113
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "APEX", peptide_summary = "sum", 
         fasta = system.file("extdata", "example.fasta", package = "aLFQ"), apex_model = APEX_ORBI.apex, 
         combine_precursors = FALSE, consensus_peptides = TRUE)
  7: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 15. Error: protein_inference.ProteinInference: NSAF peptide_method (@test_Pro
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(protein_inference.ProteinInference(peptide_data, peptide_method = "NSAF", 
         peptide_summary = "sum", fasta = system.file("extdata", "example.fasta", package = "aLFQ"), 
         apex_model = NA, combine_precursors = FALSE, consensus_peptides = TRUE)$response, 
         equals(c(0.1441441, 0.4279279, 0.1818182, 0.4090909), tolerance = 0.001)) at testthat/test_ProteinInference.R:118
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: protein_inference.ProteinInference(peptide_data, peptide_method = "NSAF", peptide_summary = "sum", 
         fasta = system.file("extdata", "example.fasta", package = "aLFQ"), apex_model = NA, 
         combine_precursors = FALSE, consensus_peptides = TRUE)
  7: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  8: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 16. Error: PeptideInference: Consensus protein / transition selection (@test_
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)$peptide_intensity, 
         equals(c(100, 90, 90))) at testthat/test_ProteinInference.R:126
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)
  7: PeptideInference.default(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)
  8: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 17. Error: PeptideInference: Consensus transition selection (@test_ProteinInf
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = FALSE, consensus_transitions = TRUE)$peptide_intensity, 
         equals(c(100, 180, 170, 90, 170, 90))) at testthat/test_ProteinInference.R:135
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = FALSE, consensus_transitions = TRUE)
  7: PeptideInference.default(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = FALSE, consensus_transitions = TRUE)
  8: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ── 18. Error: PeptideInference: Summarization of peptides (@test_ProteinInferenc
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)$peptide_intensity, 
         equals(c(100, 180, 170, 90, 170, 90))) at testthat/test_ProteinInference.R:145
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  7: PeptideInference.default(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  8: data.dt[, list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt))
  
  ── 19. Error: PeptideInference: topx selection criterion (@test_ProteinInference
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)$peptide_intensity, 
         equals(c(100, 180, 170, 90, 170, 90))) at testthat/test_ProteinInference.R:151
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  7: PeptideInference.default(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  8: data.dt[, list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt))
  
  ── 20. Error: PeptideInference: loose / strict selection criterion (@test_Protei
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)$peptide_intensity, 
         equals(c(100, 180, 170, 90, 170, 90))) at testthat/test_ProteinInference.R:156
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  7: PeptideInference.default(transition_data, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = FALSE)
  8: data.dt[, list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(peptide_intensity = sum(strictnessfilter.ProteinInference(sort(transition_intensity, 
         decreasing = TRUE)[1:transition_topx], strictness = transition_strictness))), 
         by = key(data.dt))
  
  ── 21. Error: PeptideInference: More complex data (@test_ProteinInference.R#181)
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: expect_that(PeptideInference(UPS2_SRM_REP, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)$peptide_intensity, 
         equals(c(25590.3333333333, 19487.8333333333, 94881.6666666667, 178646.666666667, 
             6015030.33333333, 627038.333333333, 11635244.6666667, 736551, 1073336.33333333, 
             11974505, 3786579, 218245.666666667, 24880.3333333333, 72675.1666666667, 
             65261.5, 218894, 5963012.66666667, 8408505, 22734358, 12761857.3333333, 20272967.3333333, 
             35512577.6666667, 10497302, 3865748.33333333, 241238.333333333, 16276914.3333333, 
             15558374.6666667, 6822498.83333333, 1277186.66666667, 11749037.6666667, 21737299.6666667, 
             1846759, 1660423, 7720861.33333333, 13014537.3333333, 14559.3333333333, 67646, 
             63101.3333333333, 22536, 30531, 3684505, 59372.3333333333, 35372, 37802.3333333333, 
             1035790, 453902.333333333, 446332.333333333, 365018.666666667, 1809386.66666667, 
             9011571, 645838.666666667, 106781.333333333, 11963.3333333333, 65084.3333333333, 
             74667.6666666667, 63633, 17660313.3333333, 59667539.3333333, 24996562.3333333, 
             7886728.16666667, 2654978, 6584263.33333333, 66764209.8333333, 37522615, 
             2694749.66666667, 400530, 300637, 1759926, 784371, 1644341.33333333, 2054531.5, 
             239703.333333333, 3074869, 1143588.33333333, 63168.6666666667, 423062, 1354893.66666667, 
             169604, 110562.666666667, 25090, 140672.333333333, 173531.666666667, 40671, 
             370075.666666667, 82473.3333333333, 187492.666666667, 309984.333333333, 113787.666666667, 
             93361.6666666667, 142269.166666667, 179003.333333333, 244661.333333333, 151754.666666667, 
             548598.833333333, 1117559.5, 775374.333333333, 1002640, 244527, 600798, 17368.6666666667, 
             237515.666666667, 47599.6666666667, 87266.5, 561006.666666667, 6744154.16666667, 
             4181555.33333333, 11341209, 12986979, 1359312.66666667, 17666858.6666667, 
             144644.666666667, 2355843.16666667, 41890138.6666667, 133970.333333333, 114110.666666667, 
             124021.333333333, 208360.333333333, 6543.33333333333, 18754.3333333333, 15237.3333333333, 
             446034.333333333, 1050469.33333333, 11569710.6666667, 302367.333333333, 163573.333333333, 
             1064324, 10907174.3333333, 8312024.66666667, 24264809, 797923.333333333, 
             953073, 974526.666666667, 870937, 376157.5, 3739507.66666667, 13866442.1666667, 
             1214256.33333333, 387412.333333333, 130142, 11279567.6666667, 6411995.66666667, 
             1353513.33333333, 19411749.6666667, 18222098, 3377765, 1290837, 3380810, 
             870910.333333333, 15916711, 106819, 2900604.66666667, 7956447, 2826125, 1371032.66666667, 
             1778378, 2036743.33333333, 670967.333333333, 25590.3333333333, 19487.8333333333, 
             94881.6666666667, 178646.666666667, 6015030.33333333, 627038.333333333, 11635244.6666667, 
             736551, 1073336.33333333, 11974505, 3786579, 218245.666666667, 24880.3333333333, 
             72675.1666666667, 65261.5, 218894, 5963012.66666667, 8408505, 22734358, 12761857.3333333, 
             20272967.3333333, 35512577.6666667, 10497302, 3865748.33333333, 241238.333333333, 
             16276914.3333333, 15558374.6666667, 6822498.83333333, 1277186.66666667, 11749037.6666667, 
             21737299.6666667, 1846759, 1660423, 7720861.33333333, 13014537.3333333, 14559.3333333333, 
             67646, 63101.3333333333, 22536, 30531, 3684505, 59372.3333333333, 35372, 
             37802.3333333333, 1035790, 453902.333333333, 446332.333333333, 365018.666666667, 
             1809386.66666667, 9011571, 645838.666666667, 106781.333333333, 11963.3333333333, 
             65084.3333333333, 74667.6666666667, 63633, 17660313.3333333, 59667539.3333333, 
             24996562.3333333, 7886728.16666667, 2654978, 6584263.33333333, 66764209.8333333, 
             37522615, 2694749.66666667, 400530, 300637, 1759926, 784371, 1644341.33333333, 
             2054531.5, 239703.333333333, 3074869, 1143588.33333333, 63168.6666666667, 
             423062, 1354893.66666667, 169604, 110562.666666667, 25090, 140672.333333333, 
             173531.666666667, 40671, 370075.666666667, 82473.3333333333, 187492.666666667, 
             309984.333333333, 113787.666666667, 93361.6666666667, 142269.166666667, 179003.333333333, 
             244661.333333333, 151754.666666667, 548598.833333333, 1117559.5, 775374.333333333, 
             1002640, 244527, 600798, 17368.6666666667, 237515.666666667, 47599.6666666667, 
             87266.5, 561006.666666667, 6744154.16666667, 4181555.33333333, 11341209, 
             12986979, 1359312.66666667, 17666858.6666667, 144644.666666667, 2355843.16666667, 
             41890138.6666667, 133970.333333333, 114110.666666667, 124021.333333333, 208360.333333333, 
             6543.33333333333, 18754.3333333333, 15237.3333333333, 446034.333333333, 1050469.33333333, 
             11569710.6666667, 302367.333333333, 163573.333333333, 1064324, 10907174.3333333, 
             8312024.66666667, 24264809, 797923.333333333, 953073, 974526.666666667, 870937, 
             376157.5, 3739507.66666667, 13866442.1666667, 1214256.33333333, 387412.333333333, 
             130142, 11279567.6666667, 6411995.66666667, 1353513.33333333, 19411749.6666667, 
             18222098, 3377765, 1290837, 3380810, 870910.333333333, 15916711, 106819, 
             2900604.66666667, 7956447, 2826125, 1371032.66666667, 1778378, 2036743.33333333, 
             670967.333333333, 25590.3333333333, 19487.8333333333, 94881.6666666667, 178646.666666667, 
             6015030.33333333, 627038.333333333, 11635244.6666667, 736551, 1073336.33333333, 
             11974505, 3786579, 218245.666666667, 24880.3333333333, 72675.1666666667, 
             65261.5, 218894, 5963012.66666667, 8408505, 22734358, 12761857.3333333, 20272967.3333333, 
             35512577.6666667, 10497302, 3865748.33333333, 241238.333333333, 16276914.3333333, 
             15558374.6666667, 6822498.83333333, 1277186.66666667, 11749037.6666667, 21737299.6666667, 
             1846759, 1660423, 7720861.33333333, 13014537.3333333, 14559.3333333333, 67646, 
             63101.3333333333, 22536, 30531, 3684505, 59372.3333333333, 35372, 37802.3333333333, 
             1035790, 453902.333333333, 446332.333333333, 365018.666666667, 1809386.66666667, 
             9011571, 645838.666666667, 106781.333333333, 11963.3333333333, 65084.3333333333, 
             74667.6666666667, 63633, 17660313.3333333, 59667539.3333333, 24996562.3333333, 
             7886728.16666667, 2654978, 6584263.33333333, 66764209.8333333, 37522615, 
             2694749.66666667, 400530, 300637, 1759926, 784371, 1644341.33333333, 2054531.5, 
             239703.333333333, 3074869, 1143588.33333333, 63168.6666666667, 423062, 1354893.66666667, 
             169604, 110562.666666667, 25090, 140672.333333333, 173531.666666667, 40671, 
             370075.666666667, 82473.3333333333, 187492.666666667, 309984.333333333, 113787.666666667, 
             93361.6666666667, 142269.166666667, 179003.333333333, 244661.333333333, 151754.666666667, 
             548598.833333333, 1117559.5, 775374.333333333, 1002640, 244527, 600798, 17368.6666666667, 
             237515.666666667, 47599.6666666667, 87266.5, 561006.666666667, 6744154.16666667, 
             4181555.33333333, 11341209, 12986979, 1359312.66666667, 17666858.6666667, 
             144644.666666667, 2355843.16666667, 41890138.6666667, 133970.333333333, 114110.666666667, 
             124021.333333333, 208360.333333333, 6543.33333333333, 18754.3333333333, 15237.3333333333, 
             446034.333333333, 1050469.33333333, 11569710.6666667, 302367.333333333, 163573.333333333, 
             1064324, 10907174.3333333, 8312024.66666667, 24264809, 797923.333333333, 
             953073, 974526.666666667, 870937, 376157.5, 3739507.66666667, 13866442.1666667, 
             1214256.33333333, 387412.333333333, 130142, 11279567.6666667, 6411995.66666667, 
             1353513.33333333, 19411749.6666667, 18222098, 3377765, 1290837, 3380810, 
             870910.333333333, 15916711, 106819, 2900604.66666667, 7956447, 2826125, 1371032.66666667, 
             1778378, 2036743.33333333, 670967.333333333))) at testthat/test_ProteinInference.R:181
  2: condition(object)
  3: expect_equal(x, expected, ..., expected.label = label)
  4: quasi_label(enquo(object), label)
  5: eval_bare(get_expr(quo), get_env(quo))
  6: PeptideInference(UPS2_SRM_REP, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)
  7: PeptideInference.default(UPS2_SRM_REP, transition_topx = 3, transition_strictness = "loose", 
         transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE)
  8: data.dt[, list(omni = length(run_id)), by = key(data.dt)]
  9: `[.data.table`(data.dt, , list(omni = length(run_id)), by = key(data.dt))
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 8 SKIPPED: 0 FAILED: 21
  1. Error: AbsoluteQuantification.default (@test_AbsoluteQuantification.R#6) 
  2. Error: predict.AbsoluteQuantification (@test_AbsoluteQuantification.R#13) 
  3. Error: cval.AbsoluteQuantification: Monte Carlo (@test_AbsoluteQuantification.R#26) 
  4. Error: AbsoluteQuantification.default (@test_AbsoluteQuantification.R#52) 
  5. Error: ProteinInference.default: Absolute abundance estimation of endogenous proteins with spiked-in SIS peptides: Single peptides / Concentration (@test_ProteinInference.R#9) 
  6. Error: ProteinInference.default: More complex data (@test_ProteinInference.R#35) 
  7. Error: protein_inference.ProteinInference: Consensus peptide selection (@test_ProteinInference.R#57) 
  8. Error: protein_inference.ProteinInference: Combine precursors (@test_ProteinInference.R#66) 
  9. Error: protein_inference.ProteinInference: Summarization of peptides (@test_ProteinInference.R#71) 
  1. ...
  
  Error: testthat unit tests failed
  Execution halted