Sorry if this should have been sent directly to Zhijin Wu, but here goes... Is there another resource for learning what the gcrma package is all about? Like the theory/rationale behind it and why/how it is better than rma? I must be doing something wrong, but my rma expression levels are the same as what I got from gcrma. Granted I just ran: data <- ReadAffy() eset.rma <- rma(data) eset.gcrma <- gcrma(data) But the expression levels generated were identical. I didn't believe it, so I'm re-running the analysis just to make sure. It's running now, but since gcrma takes so much longer to run, I thought I'd throw this question out now. I've read the "Textual description of gcrma" pdf file, but it's so cursory I'm still left wondering if I just didn't miss the more detailed explanation of the package. Stan Smiley stan.smiley@genetics.utah.edu

The two routines vary on how the background adjustment is carried out. If you view computing expression values as a three stage process 1. Background Correction 2. Normalization 3. Summarization then in the case of rma() step 1 is a convolution model step 2 is quantile normalization step 3 is a robust multichip model fit using median polish in the case of gcrma() step 1 is a based on a model using GC content step 2 is quantile normalization step 3 is a robust multichip model fit using median polish I would not expect the exact same values from both methods. Thanks, Ben On Fri, 2003-12-12 at 09:22, Stan Smiley wrote: > Sorry if this should have been sent directly to Zhijin Wu, but here goes... > > Is there another resource for learning what the gcrma package is all about? > Like the theory/rationale behind it and why/how it is better than rma? > > I must be doing something wrong, but my rma expression levels are the same > as what I got from gcrma. > > Granted I just ran: > > data <- ReadAffy() > eset.rma <- rma(data) > eset.gcrma <- gcrma(data) > > But the expression levels generated were identical. I didn't believe it, so > I'm re-running the analysis just to make sure. It's running now, but since > gcrma takes so much longer to run, I thought I'd throw this question out > now. > > I've read the "Textual description of gcrma" pdf file, but it's so cursory > I'm still left wondering if I just didn't miss the more detailed explanation > of the package. > > > Stan Smiley > stan.smiley@genetics.utah.edu > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor

Hi, Stan, > > Is there another resource for learning what the gcrma package is all about? > Like the theory/rationale behind it and why/how it is better than rma? here's the paper about the theory behind gcrma http://www.biostat.jhsph.edu/~ririzarr/papers/gcpaper.pdf I am also surprised that you got the same expression values as the rma did. If you send, for example, one array of your data, I may be able to test it. best, Jean