James, very nice example. 

 

I'd like to relate a caveat from my experience about using density plots
of gcrma-processed data. The default behavior for gcrma() or justGCRMA()
is to use a fast ad hoc algorithm for background adjustment (running
args(gcrma) yields a bunch of arguments, but the argument fast is set to
TRUE by default). I have found that density plots of data processed
using this ad-hoc method can be somewhat misleading. If you run:

 

x <- hist(exprs(eset.gcrma)[,1],breaks=seq(0,14,by=0.01))

plot(x$mids,x$freq,log='y')

 

you can see that the background peak in the density plot is not a smooth
Gaussian but something that looks more like an exponential, with the
smallest value having the highest frequency, with frequency decreasing
as the expression value increases. Interestingly, when you run gcrma or
justGCRMA with fast=FALSE (using the empirical Bayesian background
adjustment), the density plot more closely matches the actual
distribution for the background peak. Incidentally, with regards to
Casper's question, boxplots of the expression values get a lower tail
again.  However, I don't know if there's consensus on which algorithm is
more desirable, all other things being equal. 

 

 

 

Paul Williams, Ph. D.

Bioinformatics Scientist

Compendia Bioscience, Inc.

110 Miller Ave.

Ann Arbor, MI 48104 USA

pwilliams@compendiabio.com

http://www.compendiabio.com

 


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