Hi Michael, I am following up on our previous discussion, I ran DESeq2 with minReplicatesForReplace=Inf and cooksCutoff=FALSE and it actually increased the number of DE genes. 

Here is the sample code

dds <- DESeqDataSetFromMatrix(countData = countsMatrix, colData = colData,
                              design = ~ type);
dds <- DESeq(dds);
Gres <- results(dds, contrast=c("type","ABCD_DIF","ABCD_UND"), cooksCutoff = FALSE);

The big difference between deseq and deseq2 is there thousands of DE genes, even with 0.01 FDR. Is there any other criteria to filter the number of DEGs. The MA plots look fine (most of the genes are on x-axis and DE genes are colored RED)

hi Prasad,

This is not surprising that the number of DE genes increased, as you have turned off outlier filtering. 

That you have many genes with small false discovery rate means that the fold changes are large between conditions, in particular large with respect to the within-group dispersion, and that your experiment was sufficiently powered to discover many differences. 

I would follow the suggestion in my previous response:  "You can reduce the size of the list you are interested in by either lowering the alpha or using the lfcThreshold argument of results()." A: deseq2 - many differentially expressed genes

results(dds, lfcThreshold=1)