Filter on Fold Change
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@quentin-anstee-1257
Last seen 9.7 years ago
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
genefilter affy gcrma genefilter affy gcrma • 1.2k views
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@stephen-henderson-71
Last seen 7.0 years ago
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@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 _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor ********************************************************************** This email and any files transmitted with it are confidentia...{{dropped}}
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Hi Stephen, I had read that discussion but misunderstood the conclusions. On re- reading it, I think you are correct: filtering on fold change in this situation is bad! Many thanks, Quentin > -----Original Message----- > From: Stephen Henderson [mailto:s.henderson at ucl.ac.uk] > Sent: 23 February 2006 12:36 > To: Quentin Anstee > Cc: bioconductor at stat.math.ethz.ch > Subject: RE: [BioC] Filter on Fold Change > > 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 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > > ********************************************************************** > This email and any files transmitted with it are confident...{{dropped}}
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