I realize that it can be hard to nail down a source/source of this
(possible) error.

So, the data is four single-end runs - two treated and two control-treated
samples. The design I set up looks like this:
                                             condition replicate
./treatedsample1.txt          Treatment         1
./treatedsample2.txt          Treatment         2
./controlsample1.txt           Vehicle             1
./controlsample2.txt           Vehicle              2

After using read.HTSeqCounts I use estimateSizeFactors, followed
by estimateDispersions. For estimateDispersions I get the same results
whether I set minCount to 0 or 10 and whether I set maxExon to default 70
or something higher, like 1000. I have not tried to change initialGuess,
since I don't really grasp what it means. I also leave formula unchanged.
Afterwards I use fitDispersionFunction and testForDEU with default
parameters.

My ExonCountSet was created with the package's Python scripts. I used a GFF
file from UCSC which is of the same genome build as used for the read
mapping.

There is variance across my replicates, but it doesn't seem too extreme. I
could, as mentioned, call differentially expressed genes with DESeq without
problems. Do the values for dispBeforeSharing, dispFitCoefs, or dispFitted
tell me anything about this?

On 13 December 2012 10:14, Steve Lianoglou
<mailinglist.honeypot@gmail.com>wrote:

> Hi,
>
> It's hard to provide any meaningful help, since we really don't have
> any information about your data, or what you code (code examples) to
> identify a problem.
>
> But:
>
> On Thu, Dec 13, 2012 at 10:08 AM, Philip [guest] <guest@bioconductor.org>
> wrote:
> >
> > Hello,
> >
> > I'm trying to use DEXSeq to identify alternative exon usage. Using DESeq
> I've identified ~200 differentially expressed genes in my gene set. I've
> basically applied the guidelines from the manual to my data set - single
> reads in duplicates +/- treatment.
>
> Does that mean you have 4, singe end read runs? 2 biological
> replicates for (+) treatment, and 2 for (-) treatment?
>
> > I've played around with the parameters in different ways,
>
> Which parameters?
> What ways?
> How did you count reads / bin?
> How did you define the bins?
>
> > but no matter how I do it the adjusted p-values all come out as 1 or
> N/A. The non-adjusted p-values are pretty high, so I reckon the adjusted
> p-values are "true", however, when I go true single genes I find exons that
> have really high fold-change values indicating differential expression.
>
> High dispersion values can make high fold changes statistically
> insignificant.
>
> Did you explore the quality of your replicates? How? How do they look?
>
> > Is this a result one can expect (due to e.g. high variance in replicates)
>
> That can explain it.
>
> There is, of course, always the possibility that there is very little
> differential splicing in your experiment.
>
> > or is it possible that something is wrong in my analysis?
>
> It is always possible that there is something wrong with an analysis.
> As I mentioned at the start, without knowing more about your analysis
> and seeing the code, there is no way that anybody can answer this
> question.
>
> HTH,
> -steve
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>

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