[BioC] "automatic association analysis"

[Jianping Jin]

Hi Weiwei and Francois,

If my understanding is correct, you worried about false positive results, 
don't you. If that is the case we usually use Benjamili & Hochberg fdr to 
correct raw p-values which have been obtained with hypergeometirc test for 
GO analysis. We do that manually in R/Bioconductor or even in Microsoft 
Excel.

cheers!

Jiaping

--On Friday, August 25, 2006 2:09 PM -0400 Francois Pepin 
<fpepin at cs.mcgill.ca> wrote:

> Hi Weiwei
>
>> My initial question is about
>> how to automatic "validate" or "test" the result I get from whatever
>> methods i use, like text mining or something like that.
>
> I think some packages may exist, but we do that by hand. Once we're
> pointed to a specific pathway, we prefer to let humans handle the rest.
>
>> how do u define the "success events" in hypergeometric test? and how
>> do you make sure the sampling has no bias when you pick genes in your
>> study?
>
> That's one of the tricky issues. People usually use differentially
> expressed genes, but putting a threshold there isn't obvious. One of the
> reasons some people do not like it (and I'm starting to feel the same
> way) is that the values are very continuous such that changing the
> threshold by a hair changes your set of genes (often changing your
> results significantly.
>
> I'm not sure what you mean about the sampling bias. If you filter in an
> unbiased way and set your universe to be what is available on the chip
> you should be ok. You should also deal with duplicate probes (if any)
> and duplicate probes per genes (if any). Again the archives have a
> couple of fairly detailed discussions on those issues.
>
> Francois
>
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##################################
Jianping Jin Ph.D.
Bioinformatics scientist
Center for Bioinformatics
Room 3133 Bioinformatics building
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