I have two (or more) micro array data of genes of SARS (http://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE1739) and Parkinson disease (http://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE7621). I found the dysregulated genes in these sets by applying criteria that log fold change is greater than 1.5 and p-vlaue < 0.01. My question is how to find the common dysregulated genes in these two sets? Which statistical tests should be applied? Which packages are available in R for this kind analysis? I am new to bioinformatics. Kindly bear with me if question is very basic. Thanks in advance.
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written
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pankajnarula84 •
1 answer
The short answer is that you would use either the hypergeometric distribution (see ?phyper in R), or you could make a contingency table and use a Fisher Exact test. Given a universe of genes in two experiments, if you identify a set of genes in experiment 1, and another set of genes in experiment 2, these can help you evaluate the likelihood of a given degree of overlap. There's also a package in R which does this for you, called GeneOverlap.
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