Dear Community,

I currently analyzing in R, a small number (6 samples-2 conditions-3 biological reps of each condition) of CEL files regarding Affymetrix Human Gene 2.0 ST arrays (for the first time this type of gene chip arrays). A relevant subset of my code is the following:

library(oligo)
library(affycoretools)
library(hugene20sttranscriptcluster.db)

library(limma)

librarypd.hugene.2.0.st)

setwd(mydir)

pdat <- read.table("pdat.project.txt",header=TRUE,stringsAsFactors = FALSE) # phenotype info

celfiles = list.celfiles()

affy.cels <- read.celfiles(celfiles)

identical(colnames(affy.cels),rownames(pdat)) # need to be identical for incorporate phenotype info

pd <- AnnotatedDataFrame(data= pdat)
phenoData(affy.cels) <- pd
celfiles.rma <- rma(affy.cels, target="probeset")

Thus, my main questions are the following:

1) For the rma function, which is the most valid/appropriate choise of target argment for gene ST arrays ? "probeset" or "core" ?

2) For removing the control probesets, i can use the function getMainProbes ? 

3) To annotate in later steps of limma (i.e after topTable) my probesets/transcripts into gene symbols, i should first:

annotation(eset.rma) <- "hugene20sttranscriptcluster.db"

& then query the above db with functions select, etc ?

Thank you in advance !!

1.) For the vast majority of users, the 'core' argument is the way to go. The Gene ST arrays are intended to measure transcript abundances, and the ability to summarize at the probeset level is really just due to the fact that they are based on the Exon ST platform.

2.) Yes. Why do you have doubts?

3.) If you are using affycoretools, it's easier to do

library(hugene20sttranscriptcluster.db)
eset.rma <- annotateEset(eset.rma, hugene20sttranscriptcluster.db)

And then your topTable will automatically contain annotation data.