[BioC] batch effect on variances

[Lana Schaffer]

Hi,
This is just a case of matching profiles during something
like a time course.  In packages like GeneSpring and Spotfile the user
is allowed to chose a profile and then find other "genes" with matching
profiles for varying correlations.  In this case I have pvalues that are
significant for differential expression at short time periods and not
significant at long time periods.  I want to get the list of genes with that
profile.
Alternatively, the log expression ratio is high at short time period and
then levels off
at long time periods for all the genes of interest.  I thought that the high
pvalues
was due to increased variance and therefore heterogeneity. I don't know how
to
think about the decreased expression along with this, since I am dealing
with
differential expression.  I have done co-expression analysis for all these
genes and
find them to be co-expressed between 2 modules.  Show the goal is to show
levels of hetergeneity between the time periods.

I am wondering if I used limma correctly for I divided up the samples into
3 "time periods" and then then fit the samples together.  I then used
contrasts to get adjusted pvalues for the genes for the 3 "time periods".
When
I graphed the trends in pvalues for each of the genes over time I get
profiles which
increase and then flatten for a set of genes (I want to get that set of
genes)
and then other profiles.  I want to show that the
variance (hetergeneity) increases with time with some of the genes.
I think that I could do a multivariate regression to indicate a regression
in
differential expression, but then if the ratio is leveling off then
regression
won't tell me anything.
I hope you can understand where I am going.
Lana


----- Original Message ----- 
From: "Robert Gentleman" <rgentlem at fhcrc.org>
To: "Lana Schaffer" <schaffer at scripps.edu>
Cc: <bioconductor at stat.math.ethz.ch>
Sent: Tuesday, September 26, 2006 9:02 AM
Subject: Re: [BioC] batch effect on variances


>
>
> Lana Schaffer wrote:
>> Hi,
>> I want to find out if there is a batch effect (FEM or REM) on the 
>> variance for 2 sets of
>> data which are discrete (different) treatments (time).  The GeneMeta 
>> package is designed
>> to combine batches which measure the same treatment effects.  However, I 
>> have what
>> corresponds to 2 different treatment effects.  Is it valid to check 
>> homogeneity for the 2 batches?
>
>  Hi,
>    You can do some things, but I am not sure why you care? If the two 
> experiments do not have the same treatments then there is no sensible 
> analysis that combines them, so whether or not the variances are the same, 
> seems like an odd question, at least to me.  What would you want to say 
> about it and how might you try and use it?
>
>   You can fit an appropriate model to each gene in each experiment 
> separately, say using limma or any of the multitude of packages in BioC to 
> do this. Once that has been done, you can estimate per gene variances, and 
> then their ratio, suitably normalized will almost surely follow some form 
> of F statistic (provided that samples are not too small and that the 
> models are reasonable).  But I am still not sure what you would do with 
> such information.
>
>   best wishes
>     Robert
>
>
>>
>> Lana Schaffer
>> Biostatistics/Informatics
>> The Scripps Research Institute
>> DNA Array Core Facility
>> La Jolla, CA 92037
>> (858) 784-2263
>> (858) 784-2994
>> schaffer at scripps.edu
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
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>
> -- 
> Robert Gentleman, PhD
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M2-B876
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> rgentlem at fhcrc.org
>