Dear Bioconductor community,
recenltly i started analyzing a dataset which has annotation: "primeview", which is a perfect-match-only(PM) array. Thus, i cant use filtering on present/absent calls with mas5calls function from the mas5algorithm, to filter based on absent/present calls. Is there another alternative methodology based on intensity but not on variance for filtering prior to statistical inference, regarding PM only arrays ? I found a paper by Wu, Z. and R. A. Irizarry (2005) about half-price, but i couldnt find any vignette or tutorial about the code or anything.
My second and also important question refers to a methodology i used both on HG-U133a & also HGU-133aplus2 microarray platform datasets, after normalization with GCRMA(as they both have PM & MM probes. More specifically, for instance(about the hgu133a)
eset <- gcrma(cancer_data) # cancer_data my affybatch object
mas5.eset <- mas5calls(cancer_data)
call.matrix <- exprs(mas5.eset)
( ifelse(call.matrix == 'A',1,0) | ifelse(call.matrix == 'M',1,0) )
AbsentCalls.index <- which(AbsentCalls.index == TRUE)
exprs(eset)[AbsentCalls.index] <- NA
filtered.eset <- eset
My basic consern is (because im new in r) if with the above code i "mark" correctly as NA absent & present calls, because i use a different normalization algorithm than mas5 ??
Moreover, another important problem that i enganged during quality control with the primeview genechip:
during quality control, it gave me this error:
dataset.qc <- qc(dataset) # dataset is my affybatch object. Any suggestions ?
Error in setQCEnvironment(cdfn) :
Could not find array definition file ' primeviewcdf.qcdef '. Simpleaffy does not know the QC parameters for this array type.
Is this because it doesnt work with perfectmatch genechip arrays, or is something with my cdf file?