hi Kisun,

The short answer is that an experiment with no replicates is underpowered
to find differences. Under the null hypothesis, the p-values
​ ​
should be distributed uniformly from 0 to 1.  So if your p-values are
distributed
​ ​
approximately
​ ​
uniformly from 0 to 1, which you can check with:

hist(res$pvalue)

then it makes sense that the FDR will be near 1.  The fact that many FDRs
can be all the same is a consequence of the Benjamini Hochberg method, see
the discussion here:
http://permalink.gmane.org/gmane.science.biology.informatics.conductor/47450

With only two samples (control and treatment), the dispersion is estimated
by putting the two samples together as if they were replicates. So true
differences, if there are any, between control and treatment will drive up
the dispersion estimates and reduce the likelihood of rejection of the null
hypothesis. This is mentioned in ?estimateDispersions:

​   ​
estimateDispersions checks for the case of an analysis with as many samples
as the number
​   ​
of coefﬁcients to ﬁt, and will temporarily substitute a design formula ~ 1
for the purposes of
​   ​
dispersion estimation. This treats the samples as replicates for the
purpose of dispersion estimation.
​ ​

​    ​
As mentioned in the DESeq paper: "While one may not want to draw strong
conclusions from such
​   ​
an analysis, it may still be useful for exploration and hypothesis
generation."
​​


​You can also check ?plotMA which allows you to visualize the log2 fold
changes over the mean of read counts.​

​A side note, the "/extdata" part is only a necessary part of the R package
directory structure, where the data is stored which is processed by the
vignettes.  You can have your count files in any directory you wish.​


Mike


On Tue, May 28, 2013 at 1:42 PM, Kisun Pokharel <kisun.pokharel@helsinki.fi>
wrote:
>
> Hello Mike,
>
>
>
> I tried to modify your code for my data but ended up with FDR values
which are
>
> always same i.e 0.9999609. Could you please tell me where I am doing
wrong in
>
> the following code? You have explanined the pasila package but how will
it be
>
> when I have two plain text files, where first column contains ensembl ids
and
>
> second column contains counts? I have no solid R background and your help
>
> would be appreciated.
>
> Thanks!
>
>
>
>
>
> directory<-"~/Kisun/Data/Untrimmed/Deseq/extdata"
>
> sampleFiles<-list.files(directory)
>
> sampleCondition<-c("Untreated","Treated")
>
> sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles,
>
> condition=sampleCondition)
>
> ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,
>
> directory=directory, design=~condition)
>
> colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition,
>
> levels=c("Untreated", "Treated"))
>
> ddsHTSeq
>
> dds<-DESeq(ddsHTSeq)
>
> res<-results(dds)
>
> res<-res[order(res$FDR),]
>
> head(res)
>
>
>
> Kind Regards
>
> Kisun
>
>
>
> --
>
> Kisun Pokharel, M.Sc.
>
> MTT Agrifood Research Finland
>
> Biotechnology and Food Research
>
> Myllytie 1, FI-31600 Jokioinen
>
> +358503614763
>
> mtt.fi/experts/kisun_pokharel
>
> twitter.com/kisunpokharel

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