DiffBind issue: zero values for all peaks in one chromosome
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Entering edit mode
mhw46 • 0
@8b2dfd13
Last seen 3 months ago
United Kingdom

Hello,

I've been using DiffBind for my analyses and it's working great, apart from a specific problem I encountered with the output: Sometimes it will record all intervals from one "tissue" having a concentration of 0, but only for 1 chromosome. In the most recent case, it was all intervals from chr9.

I have no idea why this might be happening, as there are definitely reads throughout chr9 for this sample. If anyone has any idea what might be causing this problem, please let me know.

I have attached an image of the first 30 rows of the full dba report output csv file.

![chr9_example1


> STAT5_exp7 <- dba(sampleSheet = "DamID_exp7_HPC_diffbind.csv")
> replicate_consensus <- dba.peakset(STAT5_exp7, consensus = (DBA_TISSUE),
                                   minOverlap = 2)
> replicate_consensus <- dba(replicate_consensus,
                           mask = replicate_consensus$masks$Consensus,
                           minOverlap = 1)
> consensus_peakset <- dba.peakset(replicate_consensus, bRetrieve = TRUE)


> STAT5_exp7_reads <- dba.count(STAT5_exp7, peaks = consensus_peakset, summits = FALSE, bSubControl = FALSE, bUseSummarizeOverlaps = FALSE)
KO_WT_rep1 WT    1 narrow
KO_WT_rep2 WT    2 narrow
KO_YF_rep1 YF    1 narrow
KO_YF_rep2 YF    2 narrow
> replicate_consensus <- dba.peakset(STAT5_exp7, consensus = (DBA_TISSUE),
+                                    minOverlap = 2)
Add consensus: WT
Add consensus: YF
> replicate_consensus <- dba(replicate_consensus,
+                            mask = replicate_consensus$masks$Consensus,
+                            minOverlap = 1)
> consensus_peakset <- dba.peakset(replicate_consensus, bRetrieve = TRUE)
>
>
> STAT5_exp7_reads <- dba.count(STAT5_exp7, peaks = consensus_peakset, summits = FALSE, bSubControl = FALSE, bUseSummarizeOverlaps = FALSE)
> STAT5_exp7_analysis <- dba.contrast(STAT5_exp7_reads, contrast=c("Tissue","WT","YF"))
Computing results names...
> STAT5_exp7_analysis <- dba.analyze(STAT5_exp7_analysis, bBlacklist=FALSE, bGreylist=FALSE)
Normalize DESeq2 with defaults...
Analyzing...
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
> STAT5KO_full_report <- dba.report(STAT5_exp7_analysis, th=1, bCalled = TRUE, file = "STAT5_exp7_DB_analysis_DBv3.csv")

sessionInfo( )
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Scientific Linux 7.9 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /home/mhw46/.conda/envs/bioconductor2/lib/libopenblasp-r0.3.10.so

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

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

other attached packages:
 [1] DiffBind_3.0.15             SummarizedExperiment_1.20.0
 [3] Biobase_2.50.0              MatrixGenerics_1.2.1
 [5] matrixStats_0.61.0          GenomicRanges_1.42.0
 [7] GenomeInfoDb_1.26.7         IRanges_2.24.1
 [9] S4Vectors_0.28.1            BiocGenerics_0.36.1

loaded via a namespace (and not attached):
  [1] backports_1.4.1          GOstats_2.56.0           BiocFileCache_1.14.0
  [4] plyr_1.8.6               GSEABase_1.52.1          splines_4.0.3
  [7] BiocParallel_1.24.1      ggplot2_3.3.5            amap_0.8-18
 [10] digest_0.6.29            invgamma_1.1             GO.db_3.12.1
 [13] SQUAREM_2021.1           fansi_1.0.2              magrittr_2.0.1
 [16] checkmate_2.0.0          memoise_2.0.1            BSgenome_1.58.0
 [19] base64url_1.4            limma_3.46.0             Biostrings_2.58.0
 [22] annotate_1.68.0          systemPipeR_1.24.6       askpass_1.1
 [25] bdsmatrix_1.3-4          prettyunits_1.1.1        jpeg_0.1-9
 [28] colorspace_2.0-2         blob_1.2.2               rappdirs_0.3.3
 [31] apeglm_1.12.0            ggrepel_0.9.1            dplyr_1.0.7
 [34] crayon_1.4.2             RCurl_1.98-1.5           jsonlite_1.7.3
 [37] graph_1.68.0             genefilter_1.72.1        brew_1.0-6
 [40] survival_3.2-13          VariantAnnotation_1.36.0 glue_1.6.0
 [43] gtable_0.3.0             zlibbioc_1.36.0          XVector_0.30.0
 [46] DelayedArray_0.16.3      V8_4.0.0                 Rgraphviz_2.34.0
 [49] scales_1.1.1             pheatmap_1.0.12          mvtnorm_1.1-3
 [52] DBI_1.1.2                edgeR_3.32.1             Rcpp_1.0.8
 [55] xtable_1.8-4             progress_1.2.2           emdbook_1.3.12
 [58] bit_4.0.4                rsvg_2.1.2               AnnotationForge_1.32.0
 [61] truncnorm_1.0-8          httr_1.4.2               gplots_3.1.1
 [64] RColorBrewer_1.1-2       ellipsis_0.3.2           pkgconfig_2.0.3
 [67] XML_3.99-0.8             dbplyr_2.1.1             locfit_1.5-9.4
 [70] utf8_1.2.2               tidyselect_1.1.1         rlang_0.4.12
 [73] AnnotationDbi_1.52.0     munsell_0.5.0            tools_4.0.3
 [76] cachem_1.0.6             generics_0.1.1           RSQLite_2.2.9
 [79] stringr_1.4.0            fastmap_1.1.0            yaml_2.2.1
 [82] bit64_4.0.5              caTools_1.18.2           purrr_0.3.4
 [85] RBGL_1.66.0              xml2_1.3.3               biomaRt_2.46.3
 [88] compiler_4.0.3           curl_4.3.2               png_0.1-7
 [91] geneplotter_1.68.0       tibble_3.1.6             stringi_1.7.6
 [94] GenomicFeatures_1.42.3   lattice_0.20-45          Matrix_1.4-0
 [97] vctrs_0.3.8              pillar_1.6.4             lifecycle_1.0.1
[100] data.table_1.14.2        bitops_1.0-7             irlba_2.3.5
[103] rtracklayer_1.50.0       R6_2.5.1                 latticeExtra_0.6-29
[106] hwriter_1.3.2            ShortRead_1.48.0         KernSmooth_2.23-20
[109] MASS_7.3-55              gtools_3.9.2             assertthat_0.2.1
[112] DESeq2_1.30.1            openssl_1.4.6            Category_2.56.0
[115] rjson_0.2.21             withr_2.4.3              GenomicAlignments_1.26.0
[118] batchtools_0.9.15        Rsamtools_2.6.0          GenomeInfoDbData_1.2.4
[121] hms_1.1.1                grid_4.0.3               DOT_0.1
[124] coda_0.19-4              GreyListChIP_1.22.0      ashr_2.2-47
[127] mixsqp_0.3-43            bbmle_1.0.24             numDeriv_2016.8-1.1
DiffBind • 112 views
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0
Entering edit mode
Rory Stark ★ 4.4k
@rory-stark-5741
Last seen 4 days ago
CRUK, Cambridge, UK

Losing the reads for one tissue on one chromosome doesn't sound too good!

I notice that you are using DiffBind_3.0, which is out of date and no longer supported. Would you be able to update to the current version of R and Bioconductor and confirm that this is still an issue?

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