regarding your email to me: running pairs and finding more DEG in the case that you have one or more groups with high within-group variance is consistent with my recommendation in the Frequently Asked Questions section of the DESeq2 vignette:

http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#if-i-have-multiple-groups-should-i-run-all-together-or-split-into-pairs-of-groups

in this case I recommend comparing pairs of groups.

Thank you very much your quick response of my question.

We did filter out genes with summation of counts: rowSum=0.  Among three treatments, we find two treatments have high variations in gene expression among their 3 replicates and one treatment has small variations among its 3 replicates.

We find that using local fitType, we obtained more genes with significant differential expression, 20 of which have PCR data validations.  If we use parameter fitType, we obtained a few genes less.  The PCR data matches to local fitType better.  

More strikingly, when we run three treatments together versus split into pairs of groups in DESeq2. Together gave us much less genes, but pairs gave us more genes with significant differential expression. PCR data matches pairs results much better than together results.   

Any comment will be highly appreciated.