Hi James,

Thank you for your detailed answer which covered all the black holes on
this subject on my mind.

In fact, the problem started with the control probes. The problem was that,
when I performed limma analysis without any filters, the control probes
were on top of the differentially expressed gene list. I couldn't find out
why, it didn't seem to be an experimental defect (I concluded it from QC
Reports). So while I was trying to find out a solution for this, I also
started to think on filtering to reduce the number of multiple comparisons
(and my misunderstandings on probe design suddenly popped out, sorry for
some of the unnecessary questions.) Do you have any idea why control probes
would appear to be significantly differentially expressed? Is it logical to
just move them?

And about getting rid of the "passe" analysis pipeline; does the search for
interesting pathways start after deciding "important" genes set or is it
another approach which seeks those sets in the whole data set in a
different manner? Can you please recommend me any papers where I could
learn this approach?

Thanks again for your help and comments. Very much appreciated.

Zeynep



On Wed, Jun 20, 2012 at 5:46 PM, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi Zeynep,
>
>
> On 6/20/2012 5:18 AM, zeynep özkeserli wrote:
>
>> 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/<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?
>>
>
> Well, you are sort of wrong. In this context, feature means probeset, and
> each probeset is designed to interrogate either a gene transcript or a
> putative gene transcript.
>
>
>
>> 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<http://media.affymetrix.com/support/technical/datasheets/hgu133arrays_datasheet.pdf>
>> )
>>
>
> There is no telling if you did it right or wrong, as you neglected to show
> us your code. What you did and what you think you did may actually be
> different things. I can tell you this:
>
> > length(unique(Rkeys(**hgu133plus2ENTREZID)))
> [1] 42094
>
> So there are 42,094 unique Entrez Gene IDs represented on this array. Note
> carefully that Affy states '47,000 transcripts and variants', so they
> include transcript variants in that count, and these transcript variants
> will by definition have the same Entrez Gene ID.
>
>
>
>> - Can anybody suggest a mind-map to follow while performing non-specific
>> filtering? I think this must be done very carefully.
>>
>
> Agreed. I have never personally been fond of non-specific filtering, as to
> my mind it is a fairly blunt ax where a scalpel is required. Additionally,
> it is intended to 'fix' problems that I am not sure are either fixable or
> even exist.
>
> For instance, removing duplicated genes assumes that any feature with the
> same Entrez Gene is by definition intended to measure the same thing. If
> there were no transcript variants this would be true. But there are
> transcript variants, so you end up removing things that may well be
> measuring different things. Not much of a fix IMO.
>
> In addition, one rationale for filtering genes is to reduce the number of
> multiple comparisons. This makes sense to a certain extent if you are
> simply computing a statistic of some sort and then ranking genes in a
> univariate manner. I say to a certain extent because things like FDR are
> monotonic transforms - you aren't changing the order, just moving the
> cutoff between 'interesting' and 'uninteresting'. That's sort of passe
> these days - instead of looking for individual genes, we have moved on to
> looking for perturbed pathways or gene sets, and for that I think removing
> data is a hindrance not a help.
>
>
>
>> 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'm not sure where you got that idea, but I think it is wrong. Why would
> having more than one feature that purports to measure transcript from the
> same gene represent non-specific hybridization? It might represent
> duplicate measurement of the same thing, which would be bad because you are
> increasing the number of comparisons without actually comparing more things.
>
> You might be talking about features that might measure more than one
> transcript, and these may well exist. In fact, the probeset IDs are
> supposed to alert you to this possibility:
>
> http://www.affymetrix.com/**support/help/faqs/hgu133_2/**faq_7.jsp<http://www.affymetrix.com/support/help/faqs/hgu133_2/faq_7.jsp>
>
> The short version of that FAQ is that _a_at indicates the probeset may
> bind to multiple transcripts of the same gene, the _s_at indicates that the
> probeset may bind to multiple transcripts from the same gene family, and
> the _x_at indicates that the probeset may bind to multiple transcripts from
> unrelated genes.
>
> For that you can either take these probesets with a grain of salt, or you
> might look at the MBNI remapped cdfs, which attempt to remove probes that
> behave poorly.
>
> Best,
>
> Jim
>
>
>
>> 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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>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>

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