[BioC] Differential Gene Expression Analyses

[Sean Davis]

Hi, Avoks.

This is something that could be accomplished using a linear modeling
framework, so limma might be a good tool to use.  However,
automatically determining the experimental design may be difficult.  I
do not know of an automated system for analyzing datasets of arbitrary
complexity that does not require human intervention.

Sean


On Wed, Sep 14, 2011 at 12:03 AM, Avoks AO <ovokeraye at gmail.com> wrote:
> Hi,
>
> Is it possible to have a single protocol to analyze differential gene
> expression for experiments of different design? A dataset like GDS3715
> in GEO, for example, has both levels and sub-levels (agents).  One of
> the levels, say insulin resistant, is divided into sub-levels treated
> and untreated samples. GDS162 on the other hand is grouped into just
> two levels(no sub-levels). Running res = sam(gdseset,
> gdseset$disease.state)works fine for data with just levels.  res =
> sam(gdseset, gdseset$agent) understandably groups everything into 2
> classes, treated and untreated, which doesn't make much sense, to me
> anyway. And using res = sam(gdseset, gdseset$disease.state$agent)
> doesn't work. Is there a way to possibly identify, correctly assign
> and pair up such sub-level data if and when the script comes across
> it?
>
> Thanks.
>
> -Avoks
>
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