[BioC] vennDiagram Statistics Advice

[Noah Dowell]

Dear All,

I have used the excellent limma package to analyze my 2-color Agilent Yeast microarray data and have determined the differentially expressed genes (as compared to wild-type expression) in four independent experiments.  I am showing the summary of my results below:

> results <- decideTests(fit2, method="global")

> summary(results)

# mutant1 mutant2 mutant3  deletionstrain
#-1       147       126        40        252
# 0       5924      6033      6171       5600
# 1        185        97        45        404


The three mutant experiments represent expression data from cells expressing different point mutants in the gene that is deleted in the expression strain.  The point mutation in Mutant3 is a control that should not affect the protein's function given our current knowledge of how the protein works therefore the relatively small number of genes differentially expressed as compared to wild-type is consistent with our using that strain as a "control."

Mutants1 and 2 are similar in their biological defects.  They are also hypomorphic alleles as compared to the complete deletion strain so the smaller number of differentially expressed genes is again consistent with our working model.

I have created vennDiagrams of these four experiments to look at the overlap of differential expression between experiments.

My question is if there is a test I can run on the vennDiagrams (or simply on the overlapping gene lists) to show that there is significantly more overlap between mutant 1 or mutant 2 and the deletion strain when compared to mutant 3?

Thank you for your time and input!

Noah