Dear all, I have a burning question wrt the use of gene expression pattern standardization (i.e. subtracting mean or meadian and dividing by SD) as is commonly done prior to clustering. During gene selection steps I typically use a fold-change cut off on a list of differentially expressed genes to further enrich the list for candidate genes with larger fold changes. I have noticed a very strong positive correlation between fold-change and variance (i.e. genes with a big fold change have big variances). However, when I plot the fold changes of standardized patterns, the correlation between fold-change and variance has obviosuly been elimated. I was wondering if a fold- change filter would still be meaningful once the patterns have been standardized and if not what would the reasons for this be (i.e. why is it not meaningful anymore)? Any feedback is much appreciated! Sincerely, Johan Department of Molecular and Cell Biology University of Cape Town South Africa

On Fri, Sep 19, 2008 at 9:56 AM, Johan van Heerden <jvhn1 at="" yahoo.com=""> wrote: > > Dear all, > > I have a burning question wrt the use of gene expression pattern standardization (i.e. subtracting mean or meadian and dividing by SD) as is commonly done prior to clustering. > > During gene selection steps I typically use a fold-change cut off on a list of differentially expressed genes to further enrich the list for candidate genes with larger fold changes. I have noticed a very strong positive correlation between fold-change and variance (i.e. genes with a big fold change have big variances). However, when I plot the fold changes of standardized patterns, the correlation between fold-change and variance has obviosuly been elimated. I was wondering if a fold-change filter would still be meaningful once the patterns have been standardized and if not what would the reasons for this be (i.e. why is it not meaningful anymore)? Fold change is related to the variance, yes. More highly variable genes can (but might not) show higher fold changes, while genes with lower variance will necessarily show lower fold changes on the average (but these changes could still be highly significant). When gene expression is standardized, the variance across the genes is set to be equal; the fold changes will be similarly affected, on average. When clustering standardized gene expression, the clustering no longer reflects the original relative changes. This can be fine for making a heatmap look nice, but it has the disadvantage of distorting how "good" a gene looks (depending on your definition of "good"). Hope that helps. Sean