Hi All,

I am trying to apply Non-Specific Filtering to Affymetrix GeneChip hgu133
plus2 data.

Since it has been shown that there are multiple probe sets mapping to the
same gene in Affymetrix GeneChips (ref:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1784106/), I thought it is
necessary to filter those. So I decided to use nsFilter{geneFilter}.

First I preprocessed the data, obtained an ExpressionSet object and then I
set my criterion as it was suggested as an example for nsFilter.

- used require.entrez= TRUE, which filters out features without Entrez Gene
ID's.
- used remove.dupEntrez=TRUE, which filters features mapping to the same
Entrez Gene ID. (I turned off the variance filter to see how many will be
removed because of mapping to the same Entrez Gene ID.)

And,

- first filter removed 13009 features
- second filter removed 21629 features.

"feature" here being genes. Because this filter is under geneFilter, which
filters genes :). Am I wrong?

And here are my questions:

- If I did not perform the filtering wrongly, is it possible that there are
this many duplicates? Or is it really too many? Because in hgu133 arrays
data sheet It says that "Analyzes the relative expression level of more
than 47,000 transcripts and variants, including more than 38,500 well
characterized genes and UniGenes."
(ref:
http://media.affymetrix.com/support/technical/datasheets/hgu133arrays_datasheet.pdf
)

- Can anybody suggest a mind-map to follow while performing non-specific
filtering? I think this must be done very carefully.

And another question regarding the filtering process.

To my understanding, we should not use features mapping to the same Entrez
Gene ID, because they represent non-specific hybridization, thus they give
exaggerated signal intensities. So, does it effect preprocessing? If it
does, is it meaningful to filter them out after the preprocessing step? Or
am I doing it wrong from the first step? Should this filtering be done
before the preprocessing?

I am a little puzzled here. So any help would be appreciated.

Thank you,

Zeynep Ozkeserli
Ankara University Biotechnology Institute
Genomics Unit

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