Hello folks, I am wondering why deseq2 and edgeR results are not agreed to each in terms of FDR while both calculated almost same LogFC and Nominal/Raw p-values ?? It seems DESeq2 has more stringent view to compute the FDR and control the genes than edgeR. I ran analysis with the same initial filtering applied in edgeR and further used the same filtered data for DESeq2 with cooksCutoff=F, independentFiltering=FALSE in results function.

Parameters Used in both methods are:

rowSums(cpm(data)>1) >= ncol(data)/2

Sample = 6; each have 3 replicates ; group = C and T

adjust.method = "BH"

Dispersion , Model fit and testing in both methods applied = GLM and LRT

In particular for edgR:

y <- estimateGLMCommonDisp(y,design)

y <- estimateGLMTrendedDisp(y,design)

y <- estimateGLMTagwiseDisp(y,design)

fit <- glmFit(y,design)

lrt <- glmLRT(fit,coef=2)

In particular for DEseq2:

ddsLRT <- DESeq(dds, test ="LRT", reduced = ~1)

resLRT <- results(ddsLRT, cooksCutoff=F, independentFiltering = F)

Results:

No. of DE genes detected in edgeR ( Raw p-value <= 0.05) = 16 # I am only showing here 9 genes/miRNAs.

No. of DE genes detected in edgeR ( FDR <= 0.05) = 8

No. of DE genes detected in edgeR ( FDR <= 0.05) = 4

Note: Basically i am wondering about last 5 genes/miRNAs. Any clue ??