Dear BioC list,

Before turning to use altcdfenv package, I used genefilter package to
generate a "filtered" expressionSet, but after filtration and annotation, it
still contained lots of blank gene symbols. Some of those probesets without
symbols are from S.Pombe, so that is the reason going back to use
altcdfenvs. Furthermore, I have checked the annotated+expression value table
(before filtration, total=10928 probesets), it has abnormally high numbers
of NA gene names--- 4707...I checked ORF as well, which gave me 5222 blank
ORFs....
Please, can anybody give me any idea that how can I deal with this issue??
Many many thanks!!!

Best,
Hui-Yi


*Codes*:
## data importing

> library("affy")
> library("limma")
> targets <- readTargets("fed_total.txt")
> aaa <- ReadAffy(filenames = targets $ filename)
> eset <- rma(aaa)
>

#### add annotation info and output as a table

> library("annotata")
> library("yeast2.db")
> annotation(eset) <- "yeast2.db"
>
              #skip following codes for creating a data frame for exporting
to excel...

### filtration
library("genefilter")
f1 <- anyNA
f2 <- pOverA(0.25, log2(100))
ff <- filterfun(f1, f2)
selected <- genefilter(eset, ff)
esetsub <- eset[selected,]

## limma fit and contrast
library("limma")
design <- model.matrix(~0+factor(c(1,1,2,2,3,3)))
colnames(design) <- c("wt", "mut.a","mut.b")
fit <- lmFit(exprs(esetsub), design)
fit2 <- eBayes(fit)
contrast.matrix <- makeContrasts("mut.a vs wt" = mut.a-wt,
                                "mut.b vs wt" = mut.b-wt,levels=design)
    #skip following codes for DEG analysis

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