Hi,

On Wednesday, June 5, 2013, lucia kwak wrote:

> Dear Dr. Anders,
>
> Hello. I am comparing the differential expression tools.  For my data set,
> there are two samples with no replicates. For DESeq, since I am doing the
> multiple test, I think I should use adjusted p-value rather than p-value.
> However if I choose adjusted p-value, there are only 152 genes left out of
> 23360 genes excluding NA, since for 20861 genes, they have p.adj=1. Even
> there are only 49 genes for pval<0.05. One of the tools I am comparing with
> DESeq is edgeR. If I use pval in DESeq to compare, it works pretty well,
> and I can see there are many common genes (around 80%) in these two
> packages. However, since the default p-value in edgeR is adjusted p-value
> with BH method, I am not sure I can use p-value


It's not clear to me what you are saying. Are you suggesting that edgeR is
returning a large number of significant genes at a low FDR, or are the
number of significant genes roughly the same at the same FDR's between the
two packages?


> How can I solve this
> problem?


Sequence more replicates ;-)

With 0 replication you have very little power to detect differential
expression.

In DESeq you must be using the "blind" method to estimate over dispersion,
right? You can imagine how that makes it harder to detect all but the most
extreme fold changes.

HTH,
-Steve



-- 
Steve Lianoglou
Computational Biologist
Bioinformatics and Computational Biology
Genentech

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