ChIPQC error in previously working code (BAM association error)
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Coby Viner ▴ 50
@coby-viner-10939
Last seen 11 weeks ago
University of Toronto, Canada

I obtain the following error when trying to create a ChIPQC object:

"Unable to process. Each bam file must be associated with at most one peakset."

I am using the latest version of ChIPQC. This previously worked with version 1.14.0. Has the required sample format changed or is this a regression that can be fixed?

The below log shows all of my commands.

> samples <- read.delim("QCexperiment.csv", stringsAsFactors=FALSE)
> experiment <- ChIPQC(samples, annotation="hg38")
ENCFF863PSQ DOHH2 CTCF   1 bed
Error in ChIPQC(samples, annotation = "hg38") :
  Unable to process. Each bam file must be associated with at most one peakset.
> samples
     SampleID Tissue Factor Replicate                         bamReads
1 ENCFF863PSQ  DOHH2   CTCF         1 ENCFF863PSQ.sorted.markeddup.bam
    ControlID                       bamControl Peaks
1 ENCFF631ENA ENCFF631ENA.sorted.markeddup.bam    NA
> samples <- read.delim("QCexperiment.csv")
> samples
     SampleID Tissue Factor Replicate                         bamReads
1 ENCFF863PSQ  DOHH2   CTCF         1 ENCFF863PSQ.sorted.markeddup.bam
    ControlID                       bamControl Peaks

I believe that the error checking is accessing an object which lacks the column name, resulting in this error, as shown here:

> experiment = dba(sampleSheet=samples,peakCaller="bed")
ENCFF863PSQ DOHH2 CTCF   1 bed
>  experiment$config$mapQCth = 15
> meta = data.frame(t(experiment$class))
> length(unique(meta$bamRead))
[1] 0
> meta
                     X1    X2   X3 X4    X5  X6          X7   X8 X9
ENCFF863PSQ ENCFF863PSQ DOHH2 CTCF    FALSE bed ENCFF631ENA <NA>  1
                                         X10                              X11
ENCFF863PSQ ENCFF863PSQ.sorted.markeddup.bam ENCFF631ENA.sorted.markeddup.bam
            X12  X13
ENCFF863PSQ     <NA>
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /mnt/work1/users/home2/cviner/.pyenv/versions/miniconda3-latest/envs/Nat_Protoc_2021/lib/libopenblasp-r0.3.12.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] ChIPQC_1.26.0
 [2] DiffBind_3.0.15
 [3] SummarizedExperiment_1.20.0
 [4] MatrixGenerics_1.2.1
 [5] matrixStats_0.58.0
 [6] ggplot2_3.3.3
 [7] TxDb.Hsapiens.UCSC.hg38.knownGene_3.10.0
 [8] GenomicFeatures_1.42.3
 [9] AnnotationDbi_1.52.0
[10] Biobase_2.50.0
[11] GenomicRanges_1.42.0
[12] GenomeInfoDb_1.26.7
[13] IRanges_2.24.1
[14] S4Vectors_0.28.1
[15] BiocGenerics_0.36.0

loaded via a namespace (and not attached):
  [1] backports_1.2.1
  [2] GOstats_2.56.0
  [3] BiocFileCache_1.14.0
  [4] plyr_1.8.6
  [5] GSEABase_1.52.1
  [6] splines_4.0.3
  [7] BiocParallel_1.24.1
  [8] amap_0.8-18
  [9] digest_0.6.27
 [10] invgamma_1.1
 [11] GO.db_3.12.1
 [12] SQUAREM_2021.1
 [13] fansi_0.4.2
 [14] magrittr_2.0.1
 [15] checkmate_2.0.0
 [16] memoise_2.0.0
 [17] BSgenome_1.58.0
 [18] base64url_1.4
 [19] limma_3.46.0
 [20] Nozzle.R1_1.1-1
 [21] Biostrings_2.58.0
 [22] annotate_1.68.0
 [23] systemPipeR_1.24.3
 [24] askpass_1.1
 [25] bdsmatrix_1.3-4
 [26] prettyunits_1.1.1
 [27] jpeg_0.1-8.1
 [28] colorspace_2.0-0
 [29] blob_1.2.1
 [30] rappdirs_0.3.3
 [31] apeglm_1.12.0
 [32] ggrepel_0.9.1
 [33] dplyr_1.0.5
 [34] crayon_1.4.1
 [35] RCurl_1.98-1.3
 [36] jsonlite_1.7.2
 [37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [38] graph_1.68.0
 [39] chipseq_1.40.0
 [40] genefilter_1.72.1
 [41] brew_1.0-6
 [42] survival_3.2-10
 [43] VariantAnnotation_1.36.0
 [44] glue_1.4.2
 [45] gtable_0.3.0
 [46] zlibbioc_1.36.0
 [47] XVector_0.30.0
 [48] DelayedArray_0.16.3
 [49] V8_3.4.0
 [50] Rgraphviz_2.34.0
 [51] scales_1.1.1
 [52] pheatmap_1.0.12
 [53] mvtnorm_1.1-1
 [54] DBI_1.1.1
 [55] edgeR_3.32.1
 [56] TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2
 [57] Rcpp_1.0.6
 [58] xtable_1.8-4
 [59] progress_1.2.2
 [60] emdbook_1.3.12
 [61] bit_4.0.4
 [62] rsvg_2.1
 [63] AnnotationForge_1.32.0
 [64] truncnorm_1.0-8
 [65] httr_1.4.2
 [66] gplots_3.1.1
 [67] RColorBrewer_1.1-2
 [68] ellipsis_0.3.1
 [69] pkgconfig_2.0.3
 [70] XML_3.99-0.6
 [71] dbplyr_2.1.1
 [72] locfit_1.5-9.4
 [73] utf8_1.2.1
 [74] reshape2_1.4.4
 [75] tidyselect_1.1.0
 [76] rlang_0.4.10
 [77] munsell_0.5.0
 [78] tools_4.0.3
 [79] cachem_1.0.4
 [80] generics_0.1.0
 [81] RSQLite_2.2.5
 [82] stringr_1.4.0
 [83] fastmap_1.1.0
 [84] yaml_2.2.1
 [85] bit64_4.0.5
 [86] caTools_1.18.2
 [87] purrr_0.3.4
 [88] RBGL_1.66.0
 [89] TxDb.Rnorvegicus.UCSC.rn4.ensGene_3.2.2
 [90] xml2_1.3.2
 [91] biomaRt_2.46.3
 [92] compiler_4.0.3
 [93] rstudioapi_0.13
 [94] curl_4.3
 [95] png_0.1-7
 [96] tibble_3.1.0
 [97] stringi_1.5.3
 [98] TxDb.Hsapiens.UCSC.hg18.knownGene_3.2.2
 [99] lattice_0.20-41
[100] Matrix_1.3-2
[101] vctrs_0.3.7
[102] pillar_1.6.0
[103] lifecycle_1.0.0
[104] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[105] irlba_2.3.3
[106] data.table_1.14.0
[107] bitops_1.0-6
[108] TxDb.Celegans.UCSC.ce6.ensGene_3.2.2
[109] rtracklayer_1.50.0
[110] R6_2.5.0
[111] latticeExtra_0.6-29
[112] hwriter_1.3.2
[113] ShortRead_1.48.0
[114] KernSmooth_2.23-18
[115] MASS_7.3-53.1
[116] gtools_3.8.2
[117] assertthat_0.2.1
[118] openssl_1.4.3
[119] Category_2.56.0
[120] rjson_0.2.20
[121] withr_2.4.1
[122] GenomicAlignments_1.26.0
[123] batchtools_0.9.15
[124] Rsamtools_2.6.0
[125] GenomeInfoDbData_1.2.4
[126] hms_1.0.0
[127] grid_4.0.3
[128] TxDb.Dmelanogaster.UCSC.dm3.ensGene_3.2.2
[129] DOT_0.1
[130] coda_0.19-4
[131] GreyListChIP_1.22.0
[132] ashr_2.2-47
[133] mixsqp_0.3-43
[134] bbmle_1.0.23.1
[135] numDeriv_2016.8-1.1
chipqc • 106 views
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