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 ??
