[BioC] Differential expression

Wed May 31 00:50:52 CEST 2006

Jenny and others,

I am not a statistician, thus did not speak to specific methods in my
post. What I was thinking of, however, was using the invariant set
approach where a large number of genes are expected to be differentially
expressed.

What do the statisticians think?

Mark

Mark W. Kimpel MD 

(317) 490-5129 Home, Work, & Mobile

1-(317)-536-2730 FAX

-----Original Message-----
From: Jenny Drnevich [mailto:drnevich at uiuc.edu] 
Sent: Tuesday, May 30, 2006 5:54 PM
To: Naomi Altman; Kimpel, Mark William; E Motakis, Mathematics
Cc: Bioconductor Newsgroup
Subject: Re: [BioC] Differential expression

Hi Naomi & others,

This is the first I've heard (that I remember...) that loess
normalization 
is less susceptible to problems with a large percentage (~ >30%) of
genes 
changing. I'm wondering if this would be an OK method to use when
comparing 
different tissue types. I don't think there is any reason to expect the 
average regulation up vs. down to be any different, although I could
check 
this by comparing mean expression level between the tissues at various 
spans of A... I'm interested in this generally, although right now I'm 
working on a set of Affy arrays. There is a cyclical loess normalization

available, and I'd like to get any opinions as to whether this might be
a 
better way to go than quantile normalizing the tissues separately, if I 
want to find expression differences between tissues.

Thanks,
Jenny

At 12:05 PM 5/26/2006, Naomi Altman wrote:
>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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> >
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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
>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
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e-mail: drnevich at uiuc.edu