hi,

with below command i normalized my cel files,

library(affy)

affy.data = ReadAffy()
eset.mas5 = mas5(affy.data)

exprSet.nologs = exprs(eset.mas5)

colnames(exprSet.nologs)

write.table(exprSet, file="Su_mas5_matrix.txt", quote=F, sep="\t")

now i have my_mas5_matrix

with which R command i can omit Probe sets absent’ on ≥ 10 % of all microarray chips please????

Which specific Affymetrix platform you have used for your experiment?

In detail, if your platform is hgu133a or hgu133plus2, you could use the package panp

http://www.bioconductor.org/packages/release/bioc/html/panp.html

"A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date."

Alternatively, regarding your specific question, you could use:

mas5.eset <- mas5calls(affy.data) # as mas5calls can be used only on raw data and not on normalized

call.matrix <- exprs(mas5.eset)

ind <- rowSums(call.matrix=="P") >1 # in which you choose probesets to be characterized as "present" in at least one sample-or give a bigger number to exceed regarding the number of your samples

eset.filtered <- eset.mas5[ind,]

However, you could use another normalization algorithm, such as rma. Moreover, you should have in mind that combining different pre-processing methodologies, such as mas5 and a different normalization algorithm, maybe be problematic, as the mismatch probes have various problems regarding the intensity signals-but anyway, various approaches including the above have been widely used in the literature and in experimental studies and the final decision its up to you.

thank you i am working with citrus

i entered:

> library(affy)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from ‘package:stats’:

    xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,
    do.call, duplicated, eval, evalq, Filter, Find, get, intersect,
    is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax,
    pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int,
    rownames, sapply, setdiff, sort, table, tapply, union, unique,
    unlist, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> mas5.eset <- mas5calls(affy.data)
Error in mas5calls(affy.data) :
  error in evaluating the argument 'object' in selecting a method for function 'mas5calls': Error: object 'affy.data' not found
> affy.data = ReadAffy()
> mas5.eset <- mas5calls(affy.data)
Getting probe level data...

Computing p-values
Error in FUN(X[[i]], ...) : NA/NaN/Inf in foreign function call (arg 2)
>

you know, i am copying and pasting commands from tutorial and i do not know how to do these steps reasonably...i did rma by RMAExpress....really i can't mixed the commands by myself to get result and just use R script from turorial...

thank you, no i downloaded data from GEO unpacked them and went to change directory D:\New folder\data\New folder\New folder where i saved my cel files...

after that by mixing commands i never could the result unfortunately...for example GSE33964_RAW is one of my cases