I thought I would post my data because it is pretty strange & interesting. I am testing limma compared with DESEQ for the analysis of miRNA seq samples. Both are using the same design or grouping. On my other data set they roughly performed the same, limma being slightly less conservative.
For DESEQ I am using my standard code.
DE genes = 0. (7 reps group 1, 12, group 2). There is some moderate level of difference between groups judged qualitatively from PCA plots - but no DE genes is not surprising from that.
cds <- newCountDataSet( countTable2, group ) cds <- estimateSizeFactors( cds ) sizeFactors( cds ) cds <- estimateDispersions( cds ) res_1 <- nbinomTest( cds, "1", "2" ) res1sig <- subset(res_1, padj <= 0.05)
For Limma I have followed the manual and have used 3 types of normalisation.
design <- model.matrix(~group) matrix <- data.matrix(countTable2) dge <- DGEList(counts=matrix) dge <- calcNormFactors(dge) v <- voom(countTable2, design, plot=TRUE) ###### (method1) v <- voom(dge, design, plot=TRUE) ######### (method2) v <- voom(countTable2,design,plot=TRUE,normalize="quantile") ####### (method3) fit <- lmFit(v,design) fit <- eBayes(fit) top2 <- topTable(fit,coef=2,number=Inf,sort.by="P") sum(top2$adj.P.Val<0.05)
So method 1 gives me 1663 DE miRNA genes, from 2576 total. This doesnt seem right to me especially when compared with DESEQ.
Method 2 gives me 1350, still pretty strange result. Why are there so many?
Method 3 gives me 13, this seems much more similar to DESEQ. This kind of figure we would expect.
I have also tested this on another cell type which has only two reps versus like 15 and it has about 1000 DE genes with limma and none for DESEQ, this is part of the same data set. The DESEQ results seem more belivable on this data set except for when I do quantile normalisation with limma. That seems to balance the two. I find it strange that the other methods that normalise according to depth generate such high numbers of DE genes compared to quantile normalisation. My understanding of these methods is that they should give roughly similar results. Any thoughts or helpful comments?