Hi, I have been given a bunch of data; some of it is from a 384x164 chip and another that is 532x85. First question, how can I read these in and normalize them together? I can create separate target files for each set. But then how to merge? I have seen the limma section title 'Combine RGList, MAList, EList or EListRaw Objects', but since there are different rows (probes), Second, I'm using this invocation: dat = read.maimages(files=dir('data/scrubbed/', full.names=T), annotation=c("Row", "Col", "ProbeName", "SystematicName"), source="agilent.median", green.only=T) Is that any different from: dat<-read.maimages(files=dir('data/scrubbed/', full.names=T), columns=list( G = "gMedianSignal", Gb = "gBGMedianSignal"), annotation=c("Row", "Col", "ProbeName", "SystematicName"), green.only=T) ? If there's somewhere with example analyses of single channel agilent data, please let me know. I'm going off what I found in the archives. thanks, -Brent

Dear Brent, as no one has answered your e-mail so I will have a try. As to your first question of how to read in and normalize data from different chips together, these recent posts might be of help: https://stat.ethz.ch/pipermail/bioconductor/2011-August/040726.html https://stat.ethz.ch/pipermail/bioconductor/2011-August/040576.html As to your second question, by checking the source of read.maimages (just type read.maimages in R), there should be no difference of your two commands except in the second case the source in the RGlist is set to generic. The Bioc package Agi4x44PreProcess works with single channel Agilent data. But I am not sure if it will work with 384x164 or 532x85 chips. Best regards, Yong Brent Pedersen wrote: > Hi, I have been given a bunch of data; some of it is from a 384x164 > chip and another > that is 532x85. > > First question, how can I read these in and normalize them together? > I can create separate target files for each set. But then how to merge? > I have seen the limma section title 'Combine RGList, MAList, EList or > EListRaw Objects', > but since there are different rows (probes), > > Second, I'm using this invocation: > > dat = read.maimages(files=dir('data/scrubbed/', full.names=T), > annotation=c("Row", "Col", "ProbeName", "SystematicName"), > source="agilent.median", > green.only=T) > > Is that any different from: > > dat<-read.maimages(files=dir('data/scrubbed/', full.names=T), > columns=list( > G = "gMedianSignal", Gb = "gBGMedianSignal"), > annotation=c("Row", "Col", "ProbeName", "SystematicName"), > green.only=T) > ? > > > If there's somewhere with example analyses of single channel agilent data, > please let me know. I'm going off what I found in the archives. > thanks, > -Brent > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor