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.
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.