I have a count matrix of FPKM values and I want to estimate differentially expressed genes between two conditions. First I used DESeq2, but I realized that this is not good for FPKM values. I then transformed the counts using voom() in the limma package and then used:
fit <- lmFit(myVoomData,design) fit <- eBayes(fit) options(digits=3) writefile = topTable(fit,n=Inf,sort="none", p.value=0.01) write.csv(writefile, file="file.csv")
My problem is that all of the 6156 genes are differentially expressed (p-value 0.01). Only a few hundred were differentially expressed using DESe2, but I guess that can't be trusted.
I am new to this type of analysis, and to R, but is it ok to simply transform the data by voom()? Can I use the transformed data in DESeq2? Any other ways I can use FPKM counts to estimate differentially expressed genes?
> sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale:  C attached base packages:  grid parallel stats graphics grDevices utils datasets methods base other attached packages:  limma_3.18.3 cummeRbund_2.4.0 Gviz_1.6.0 rtracklayer_1.22.0 GenomicRanges_1.14.3 XVector_0.2.0  IRanges_1.20.6 fastcluster_1.1.11 reshape2_1.2.2 ggplot2_0.9.3.1 RSQLite_0.11.4 DBI_0.2-7  BiocGenerics_0.8.0 loaded via a namespace (and not attached):  AnnotationDbi_1.24.0 BSgenome_1.30.0 Biobase_2.22.0 Biostrings_2.30.1 Formula_1.1-1  GenomicFeatures_1.14.2 Hmisc_3.13-0 MASS_7.3-29 RColorBrewer_1.0-5 RCurl_1.95-4.1  Rsamtools_1.14.2 XML_3.95-0.2 biomaRt_2.18.0 biovizBase_1.10.4 bitops_1.0-6  cluster_1.14.4 colorspace_1.2-4 dichromat_2.0-0 digest_0.6.3 gtable_0.1.2  labeling_0.2 lattice_0.20-24 latticeExtra_0.6-26 munsell_0.4.2 plyr_1.8  proto_0.3-10 scales_0.2.3 splines_3.0.2 stats4_3.0.2 stringr_0.6.2  survival_2.37-4 tools_3.0.2 zlibbioc_1.8.0
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