I am analyzing some mouse Gene ST 2.0 arrays, and I would to know how many probes make up a probeset when having a vector of probesets IDs (in order to evaluate the 'reliability' of the expression measurement). For example, probeset "17200001" is comprised of e.g. 8 probes, probeset "17200003" of e.g. 6, etc. However, I don't know how to do this....(though I expect some fancy SQL querying is required...). I would appreciate it if someone could give me pointer on how to best approach this. Thanks, Guido
> celFiles <- list.celfiles(full.names = TRUE, listGzipped=TRUE) > affy.data <- read.celfiles(celFiles) Loading required package: pd.mogene.2.0.st Loading required package: RSQLite Loading required package: DBI Platform design info loaded. Reading in : ./GSM2028011_Ctrl1.CEL.gz Reading in : ./GSM2028012_Ctrl2.CEL.gz Reading in : ./GSM2028013_Ctrl3.CEL.gz Reading in : ./GSM2028014_LPS1.CEL.gz Reading in : ./GSM2028015_LPS2.CEL.gz Reading in : ./GSM2028016_LPS3.CEL.gz > > x.norm.plm <- fitProbeLevelModel(affy.data) Background correcting... OK Normalizing... OK Summarizing... OK Extracting... Estimates... OK StdErrors... OK Weights..... OK Residuals... OK Scale....... OK > > > probesets <- head(rownames(coef(x.norm.plm))) > probesets [1] "17200001" "17200003" "17200005" "17200007" "17200009" "17200011" >
Once again thanks very much, working nicely! However, I noticed this doesn't work when using a remapped (custom) PdInfo object (from the MBNI group). I suspect it is related to the problem I reported before, and for which you provided a work-around (A: Oligo: cannot fit robust Probe Level linear Models / fitPLM when using a custom ). Is a similar fix applicable here?
For the archive:
Below the issue with the remapped PdInfo:
That's a different beast.
Should do that for you. Or if you want to hang in the back of the bus with the cool kids, the SQL goes something like this