[BioC] Filter on Fold Change

[Stephen Henderson]

Hi Quentin

There has been a similar discussion over the past few days. The main
conclusion being (I think) not to filter based upon a known contrast in
your data. This will bias any multiple testing corrections you make.

What you have done so far is OK but if you were going to use something
like limma to fit a linear model to your data it would be better to fit
it all and select the interesting bits (toptable) based on your contrast
afterwards.


Stephen Henderson
Wolfson Inst. for Biomedical Research
Cruciform Bldg., Gower Street
University College London
United Kingdom, WC1E 6BT
+44 (0)207 679 6827


-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Quentin
Anstee
Sent: 23 February 2006 11:19
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] Filter on Fold Change

Dear List,
 
I have used the genefilter package to filter out uninformative probe
sets
from my GCRMA normalised affy experiment as follows.
 
f1 <- kOverA(3,6)
f2 <- function(x) (IQR(x) > 0.5)
ff<-filterfun(f1,f2)
wh<-genefilter(esetGCRMA, ff)
mySubSet<-esetGCRMA[wh,]
 
I would also like to filter out those genes that have less than a 2-fold
change in expression between any two of my three study groups *before* I
go
on to fit a linear model and test for significant differences. My aim is
to
test as few genes as possible to minimise the effect of multiple testing
correction and as I will only follow-up those with at least a 2-fold
change,
I would like to filter out the rest as soon as possible.
 
Please can you advise me whether this can be achieved with genefilter.
Also,
any advice on how to script this would also be much appreciated - I have
had
a look at the vignettes but can't find any that describe filtering on
fold
change although I see from the list archives that it is commonly done.
 
Many thanks,
 
Quentin

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