Dear Wolfgang and Simon,

Thanks very much for your replies and help! OK, let me try to clarify: 
what I'm attempting is typical for a time course where there's a control 
and an experimental group. I'm attempting to find patterns of expression 
that are specifically associated with the experimental group, not the 
control. I have paired treatments of control treatment and drug 
treatment. What I'd like to end up with is a single FDR or p-value for 
each row/gene of my heatmap  - a statistic indicating which patterns are 
significant, and which are due to chance.

My question is, can DESeq perform an ANOVA-like test with five means? If 
so, how would you recommend I set up the test? My previous attempt was 
clearly flawed...

Many thanks for your time and help with this!

Best wishes,
Elena

Message: 18
Date: Thu, 05 Jan 2012 09:38:21 +0100
From: Wolfgang Huber<whuber@embl.de>
To:bioconductor@r-project.org
Subject: Re: [BioC] DESeq for an mRNA-seq time course
Message-ID:<4F05617D.40507@embl.de>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Dear Elena

thank you for the detailed report. This is perhaps not the answer you
are expecting, but perhaps would it be better to first figure out _what_
you want to achieve before discussing the _how_?

When in a dataset like yours you test against the null hypothesis of 'no
effect of time', it is pretty plausible that the tests reject very many
genes.

For such time courses, I have often found it more instructive to cluster
the response patterns (e.g. using k-means on the profiles themselves or
on the differences between treatment and control) and then, if needed,
to set up statistical tests for a specific hypothesis induced from that
data exploration.

These tools are useful in that context:
- variance stabilising transformation of the count data (see DESeq
vignette) to bring them on a scale where the clustering metric (e.g.
Euclidean) is more appropriate,
- variance filtering, to first remove genes that do not chance
appreciably at all and thus to simplify the clustering,
- heatmaps.

Hope this helps
	Wolfgang


------------------------------

Message: 19
Date: Thu, 05 Jan 2012 10:25:57 +0100
From: Simon Anders<anders@embl.de>
To:bioconductor@r-project.org
Subject: Re: [BioC] DESeq for an mRNA-seq time course
Message-ID:<4F056CA5.7050004@embl.de>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Hi Elena

With this here

     fit1<- fitNbinomGLMs(cds, count ~ treat.type + time.hr)
     fit0<- fitNbinomGLMs(cds, count ~ treat.type)

you are testing against the null hypothesis that a gene's expression is
independent of the time of the sample, i.e., you will get a hit whenever
you can reject the claim that the gene has the same expression in all
five time points. This is a pretty narrow null hypothesis. It seems
quite plausible to me that around 50% of the genes change noticeable
over time.

Note also that treatment is a factor in both your full model and in your
null model, so you are not testing whether the treatment has an effect.
I suspect that this might not be what you want.

In a simple comparison of treatment vs control the question one wants to
test is usually fairly obvious: does the treatment influence the
expression or not. For time course data one usually does not have such
an obvious yes-or-no question, and hence, you may need to tell us more
about what exactly you want to test before we can advise you.

    Simon





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