Hi all, During Differential gene expression analysis of RNASeq data (DESEq2 or Cufdiff) which is best method to filter differentially expressed genes? Should I go with all the genes having adjusted P value < 0.05 or should I filter them based on a log2 Fold change cut-off?
Thank you
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Sreeraj Thamban
Thank you Kevin this cleared my doubts. I have one more question, I took 1.3 as fold change cutoff to filter DEGs during the analysis, is 1.3 is an acceptable fold change? or should I increase?
Thank you
Hi Kevin,
In Deseq2 to get DEGs based on FDR < 0.1 and FC > 1.5
dds <- DESeq(dds)
After this to select siginificant DEGs I have seen two analysis.
First analysis:
res <- results(dds, alpha = 0.1)
res2 <- res[res$log2FoldChange >= 1, ]
summary(res2)
out of 10789 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 148, 1.2%
LFC < 0 (down) : 92, 0.77%
Second one:
res <- results(dds, lfcThreshold = log2(1.5), alpha = 0.1)
summary(res)
out of 11949 with nonzero total read count
adjusted p-value < 0.1
LFC > 0.58 (up) : 15, 0.13%
LFC < -0.58 (down) : 5, 0.042%
outliers [1] : 81, 0.68%
low counts [2] : 7603, 64%
(mean count < 12)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
Among the both analysis which is the right one?
Yes, you are right. So, if I want to select DEGs based on FC > 1.5 and FDR < 0.1 second analysis is the right one. But in this [Filter by Fold change in DESeq2] e.rempel says that first approach is the right one.
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You can also try an integrated approach like metaseqR with more than one algorithms and combined p-values. In this way you don't have to struggle with comparisons as the method combines the "advantages" of many algorithms towards the optimization of precision-recall tradeoff. Disclaimer: I am the author of that package.
Thank you Moulos, I will definitely give metaseqR a try.