Howdy,

Disclaimer: I am not a statistician and am always reluctant to give such
advice since I'd never really claim authority in this arena, but ... here
goes ;-)


On Wednesday, August 15, 2012, Aliaksei Holik wrote:

> Dear listers,
>
> Apologies if my question is not strictly related to Bioconductor, though
> one never knows, maybe there's a package that does what I need.
>
> I am analysing a list of differentially expressed genes from an Illumina
> microarray. In particular I'm trying to compare the list of differentially
> expressed genes to an existing list of genes preferentially expressed in
> the stem cell population (stem cell signature). When I do so, 10% of DE
> genes belong to the stem cell signature. What I'd like to do now is to find
> out, how likely that would happen by chance, i.e. put a p value on it.
>
> At the moment I know:
> There're 17119 unique genes in my dataset.
> Of them 86 are differentially expressed.
>
> The stem cell signature contains 510 genes.
> It is combined from several platforms, which makes it hard to establish
> the total number of unique genes, but it's at least 20819 (the size of the
> largest platform).
>
> There are 9 overlapping genes between DE genes and the stem cell signature.
>
> So I wonder:
>
> 1) If there's an accepted way to calculate a p value using these data. For
> instance could I run a like of a chi squared test? E.g. stem cell specific
> genes represent 510/20819=2.4% of total dataset. So expected number of the
> stem cell genes in my DE genes would be 86x2.4%=2. So my chi squared test
> would be based on 9 observed vs 2 expected.


A fisher's test would seem like the natural first choice. I'm also pretty
sure that (for large enough N) the chi-square is a good approximation to
the same, so your intuition is spot on!

Your choice in numbers (ie. what the real size of "the urn" that you sample
from is) is crucial, so some more care is required there.

2) Or do I have to generate a geneset based on the stem cell signature and
> go through GSEA algorithms to calculate enrichment and significance.


These aren't mutually exclusively and sure -- if you have a "signature set"
why not add it to the pool you would compare against with GSEA and let it
rip.

The difference here is that you will need the expression values for your
genes and not just a list of DE genes for this to work (it wasn't clear to
me if you had that -- it's also not clear if your expression is coming from
different arrays or the gene set is: mixing expression from different
platforms is tricky)

 HTH,
-steve


> Any pointers in the right direction would be much appreciated.
>
> Many thanks for your time and help!
>
> Aliaksei.
>
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-- 
Steve Lianoglou
Graduate Student: Computational Systems Biology
 | Memorial Sloan-Kettering Cancer Center
 | Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact

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