Question: ChipQCreport coverage plot failure?
gravatar for tapio.envall
7 days ago by
tapio.envall0 wrote:


I'm quality-checking the mouse ChIP-seq data with ChIPQC, but for some reason the coverage plot behaves weirdly. After this, I ran the samples by chromosomes and found out it is chr Y - which has much less reads than others - which is causing the trouble. To demonstrate, here are coverage plots with all the chromosomes, with all but chr Y, and with chr Y only.

This is all the data (chr Y included)

This is data without chr Y

And this is chr Y itself.

Any ideas what is going on, is there something wrong with the data / program?


This is my code, (producing the reports in the same order as are the figures):

samples <- read.csv("samples_ChIPQC", stringsAsFactors = FALSE, header = TRUE)

exampleExp = ChIPQC(samples, chromosomes = NULL)
ChIPQCreport(exampleExp, reportFolder = "bc_all")

#"chromosomes" just lists the chromosomes, Y being the last, 21th
chrs <- (read.table("chromosomes", stringsAsFactors = FALSE))[,1]
exampleExp = ChIPQC(samples, chromosomes = chrs[-21])
ChIPQCreport(exampleExp, reportFolder = "bc_without_Y")
exampleExp = ChIPQC(samples, chromosomes = "chrY")
ChIPQCreport(exampleExp, reportFolder = "bc_chrY")

This is how 'samples' look like.

> samples
  SampleID Tissue    bamReads ControlID  bamControl        Peaks PeakCaller
1       S1     T1 s_1_001.bam   S1_ctrl S1_ctrl.bam S1_peaks.bed       MACS
2       S2     T2 s_2_001.bam   S2_ctrl S2_ctrl.bam S2_peaks.bed       MACS


> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.2

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] ChIPQC_1.16.0               DiffBind_2.8.0              SummarizedExperiment_1.10.1 DelayedArray_0.6.2          BiocParallel_1.14.2         matrixStats_0.54.0         
 [7] Biobase_2.40.0              GenomicRanges_1.32.6        GenomeInfoDb_1.16.0         IRanges_2.14.10             S4Vectors_0.18.3            BiocGenerics_0.26.0        
[13] ggplot2_3.0.0              

loaded via a namespace (and not attached):
  [1] amap_0.8-16                               colorspace_1.3-2                          rjson_0.2.20                             
  [4] hwriter_1.3.2                             XVector_0.20.0                            base64enc_0.1-3                          
  [7] rstudioapi_0.7                            ggrepel_0.8.0                             bit64_0.9-7                              
 [10] AnnotationDbi_1.42.1                      splines_3.5.0                             TxDb.Rnorvegicus.UCSC.rn4.ensGene_3.2.2  
 [13] Nozzle.R1_1.1-1                           Rsamtools_1.32.2                          annotate_1.58.0                          
 [16] GO.db_3.6.0                               pheatmap_1.0.10                           graph_1.58.0                             
 [19] TxDb.Hsapiens.UCSC.hg18.knownGene_3.2.2   compiler_3.5.0                            httr_1.3.1                               
 [22] GOstats_2.46.0                            backports_1.1.2                           assertthat_0.2.0                         
 [25] Matrix_1.2-14                             lazyeval_0.2.1                            TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2  
 [28] limma_3.36.2                              prettyunits_1.0.2                         tools_3.5.0                              
 [31] bindrcpp_0.2.2                            gtable_0.2.0                              glue_1.3.0                               
 [34] GenomeInfoDbData_1.1.0                    Category_2.46.0                           reshape2_1.4.3                           
 [37] systemPipeR_1.14.0                        dplyr_0.7.6                               ShortRead_1.38.0                         
 [40] Rcpp_0.12.18                              TxDb.Dmelanogaster.UCSC.dm3.ensGene_3.2.2 TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2  
 [43] Biostrings_2.48.0                         gdata_2.18.0                              rtracklayer_1.40.3                       
 [46] TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.0  stringr_1.3.1                             gtools_3.8.1                             
 [49] XML_3.98-1.13                             edgeR_3.22.3                              zlibbioc_1.26.0                          
 [52] scales_0.5.0                              hms_0.4.2                                 RBGL_1.56.0                              
 [55] RColorBrewer_1.1-2                        BBmisc_1.11                               memoise_1.1.0                            
 [58] biomaRt_2.36.1                            latticeExtra_0.6-28                       stringi_1.2.4                            
 [61] RSQLite_2.1.1                             genefilter_1.62.0                         checkmate_1.8.5                          
 [64] GenomicFeatures_1.32.0                    caTools_1.17.1.1                          chipseq_1.30.0                           
 [67] rlang_0.2.1                               pkgconfig_2.0.1                           BatchJobs_1.7                            
 [70] bitops_1.0-6                              TxDb.Celegans.UCSC.ce6.ensGene_3.2.2      lattice_0.20-35                          
 [73] purrr_0.2.5                               bindr_0.1.1                               labeling_0.3                             
 [76] GenomicAlignments_1.16.0                  bit_1.1-14                                tidyselect_0.2.4                         
 [79] GSEABase_1.42.0                           AnnotationForge_1.22.1                    plyr_1.8.4                               
 [82] magrittr_1.5                              sendmailR_1.2-1                           R6_2.2.2                                 
 [85] gplots_3.0.1                              DBI_1.0.0                                 pillar_1.3.0                             
 [88] withr_2.1.2                               survival_2.42-6                           RCurl_1.95-4.11                          
 [91] tibble_1.4.2                              crayon_1.3.4                              KernSmooth_2.23-15                       
 [94] progress_1.2.0                            locfit_1.5-9.1                            grid_3.5.0                               
 [97] data.table_1.11.4                         blob_1.1.1                                Rgraphviz_2.24.0                         
[100] digest_0.6.15                             xtable_1.8-2                              brew_1.0-6                               
[103] munsell_0.5.0     
ADD COMMENTlink written 7 days ago by tapio.envall0


Would you be able to share the BAM file with me on Box ( so I can try and find the source of this?



ADD REPLYlink written 5 days ago by Thomas Carroll390


Unfortunately the data is not public and I can not share it.

However, it may have something to do with how chromosomes are combined: I took two non-problematic chromosomes, and started to down-sample the other one, and ran ChIPQC for the combined file. Some "cracks" do appear, though not as drastic as in the figures I linked before. I assume that the coverage plots should approach that of the intact chromosome when inputting less and less reads from the other chromosome.

If I find time I try to reproduce this with some public data.



ADD REPLYlink written 4 days ago by tapio.envall0
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