[BioC] Differential expression

[Naomi Altman]

In many experiments, a large number of genes differentially express. 
Loess normalization will continue to work reasonably well if, at each 
level of intensity "A" the the average up and down regulation are 
about equal.  However, you would probably not want to count on this 
on small regions of the array, so it would probably be best to use a 
whole-array loess, rather than print-tip loess if many tips were used.

The problem, of course, is to understand if the average up and down 
regulation are about equal.  E.g. if you are looking at transcription 
factor mutants, this would be a very bad assumption.

--Naomi

At 11:25 AM 5/26/2006, Kimpel, Mark William wrote:
>Makis,
>
>I am speaking as a biologist, not as a statistician. Under conditions of
>most biologic experiments, the assumption is that cells need to continue
>mundane "housekeeping" functions and that these are minimally effected
>by the differential conditions of the experiment. In my area, which is
>neuroscience, we hope to see differential expression of genes involved
>with neurotransmission or synaptic plasticity, but do not expect to see
>differential expression of genes involved in just keeping neurons and
>support cells alive and intact. It turns out that most genes are
>involved in the latter, not the former, processes. We occasionally see
>examples on this list, however, where very drastic experimental
>conditions, such as one might see in toxicology, lead to differential
>expression of a larger percentage of genes.
>
>It is important, then, to put your experiment into biologic context to
>consider whether your current findings make sense and how best to
>proceed with normalization and analysis. For instance, normalization
>techniques that make sense when only a small percentage of genes are
>differentially expressed may not be appropriate when a much large
>percentage of genes are differentially expressed (and I'll let the
>statisticians on this list address what those procedures are and how to
>decide which to use when).
>
>If you would, you might describe for the list the context of your
>experiment so that others might know how best to advise you to proceed.
>
>Mark
>
>Mark W. Kimpel MD
>
>Indiana University School of Medicine
>
>.ch] On Behalf Of E Motakis, Mathematics
>Sent: Friday, May 26, 2006 11:07 AM
>To: Bioconductor
>Subject: [BioC] Differential expression
>
>Dear all,
>
>I am working on two colours microarray experiments and, from a set of
>42000
>genes, I would like to identify the differentially expressed ones. I
>have
>read several articles on this issue and most of them imply that the
>number
>of differential expressed genes in such experiments should be a small
>number (compared to the whole set).
>
>Could anyone tell me why this is correct? What if I find half of the
>genes
>to be differentially expressed according to the t-test p-value?
>
>I am not discussing the issue of p-values and q-values yet. I am asking
>only about why most of the papers imply a low number of differentially
>expressed genes.
>
>Thank you,
>Makis
>
>
>----------------------
>E Motakis, Mathematics
>E.Motakis at bristol.ac.uk
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111