Hi I am using your script to filter out the genes that are Absent in ALL the arrays. In the discussion you mention that normalize the data and then calculate detection calls(red font) but in the script, you do normalize first but for detection call you use the raw data(Blue font) is this the right step? See Below. Thanks. Mala ***************************************** Claire Wilson ClaireWilson at PICR.man.ac.uk Tue Jul 19 17:28:50 CEST 2005 Previous message: [BioC] Getting LimmaGUI to run Next message: [BioC] ignore blanks in GPR Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] ---------------------------------------------------------------------- ---------- Hi Josh, Not sure whether anyone has responded to your mail yet, but I think an easier way to do this would be to load your data in, normalise it then calculate detection calls (present/absent calls) and then use these to filter only out those probesets called present.. library(simpleaffy) library(gcrma) # read in all the cel files in the current directory raw.data <- ReadAffy() # normalise using gcrma gcrma.eset <- call.exprs(raw.data, "gcrma") # calculate detection calls # present/absent calls are stored in the $call slot calls.eset <- detection.p.val(raw.data) # show the complete present/absent calls table for the expression set, # columns are different chips, rows are the probesets calls.eset$call # summarise the calls for filtering # effectively counts how many times a particular probeset is called present calls.sum <- rowSums(calls.eset$call == "P") # to get all probesets that are present on all chips present.all.chips <- names(calls.sum[calls.sum==length(colnames(gcrma.eset at exprs))]) [[alternative HTML version deleted]]