Hi,

I'm new to microarray analysis with gene/exon type of arrays -in my
case human gene 1.0 ST arrays. Now I have to analyze 9 such arrays (3
replicates for three conditions each) and I use the "low level"
analysis as suggested in the corresponding vignette on aroma homepage

http://www.aroma-project.org/node/38

and subsequently some limma/affy code to get my "hot" genes list.

In addition to this I looked at the CEL-files using Affymetrix Gene
expression console and AltAnalyze, both using RMA-based method for
gene summary. Besides the fact that I'm now left behind with 3 very
different lists I would like to know whether the "low level" analysis
does not exclude to look a little deeper into the underlying probesets
that were used (or rejected) in order to summarize expression levels.

I installed the package hugene10stprobeset.db, this should allow me to
retrieve all relevant probesets for a given gene. Could I use this
type of information to go back to my quantile normalized
AffymetrixCelSet (csN) to retrieve the corresponding expression values
for those probesets - or is this no way to go? On the long way I'd
like to write a little exporter that allows the per probeset
extraction of the data in a form that allows to be displayed in genome
browser (like the affy exon panel on UCSC)-> something like a bed or
gff file.

Another question related to the same dataset: what type of filtering
is suggested in order to find a cut-off that might allow the
differentiation between expressed and non-expressed genes. With the
procedure as pointed out above using Aroma I get 23000 genes - most
likely not all of them are really expressed! Unfortunately the vast
amount of genes is no good for the p-value correction, basically no
genes survive this step with p-values lower than 0.1.

I'd be glad for any type of suggestion which direction I might have to
go!
Maxim

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