Hi there,
Thanks for putting together a really nice tutorial for " From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data". I had a quick question regarding the function combineTests.
When I run the example piece of code I get the exact same FDR for very different p-values (see below). In the most extreme cases with some of my data all FDR values are equal independently of the p-value. Can somebody clarify this?
Many Thanks,
Renan
library(csaw) ids <- round(runif(100, 1, 10)) tab <- data.frame(logFC=rnorm(100), logCPM=rnorm(100), PValue=rbeta(100, 1, 2)) head(tab) combined <- combineTests(ids, tab) head(combined)
Output
nWindows logFC.up logFC.down PValue FDR 1 3 0 1 0.29353630 0.4050539 2 11 4 3 0.06942862 0.3471431 3 14 8 4 0.23326239 0.3887707 4 11 6 2 0.51039737 0.5103974 5 9 1 2 0.14480150 0.3877034 6 6 1 4 0.35435897 0.4050539
`
Session Info
R version 3.2.2 (2015-08-14) Platform: x86_64-apple-darwin13.4.0 (64-bit) Running under: OS X 10.11.1 (El Capitan) locale: [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] stats4 parallel stats graphics grDevices utils datasets methods base other attached packages: [1] csaw_1.4.0 SummarizedExperiment_1.0.1 Biobase_2.30.0 GenomicRanges_1.22.1 GenomeInfoDb_1.6.1 [6] IRanges_2.4.1 S4Vectors_0.8.2 BiocGenerics_0.16.1 loaded via a namespace (and not attached): [1] AnnotationDbi_1.32.0 XVector_0.10.0 edgeR_3.12.0 zlibbioc_1.16.0 GenomicAlignments_1.6.1 [6] BiocParallel_1.4.0 tools_3.2.2 DBI_0.3.1 lambda.r_1.1.7 futile.logger_1.4.1 [11] rtracklayer_1.30.1 futile.options_1.0.0 bitops_1.0-6 biomaRt_2.26.0 RCurl_1.95-4.7 [16] RSQLite_1.0.0 limma_3.26.3 GenomicFeatures_1.22.4 Rsamtools_1.22.0 Biostrings_2.38.1 [21] XML_3.98-1.3