How to Find Common Dysregulated Genes in Two or More Sets of Microarray Data?
Last seen 5 months ago
You could make a contingency table and use a Fisher Exact test, or you could use the hypergeometric distribution (see ?phyper in R). 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. As b.nota mentioned, I usually make a Venn diagram and then evaluate it with either of those tests. There's a package in R which does this for you, called GeneOverlap.
Last seen 59 minutes ago
WEHI, Melbourne, Australia
For each gene, you can use the maximum of the two p-values from the SARS and Parkinson datasets to test whether the gene is dysregulated in both diseases.
In other words, a gene is a common significant gene if it is significant in both diseases. It is as simple as that.
However, the method you have used to assess significance in each individual dataset does not seem the best. It would be better to apply an analysis method that controls the false discovery rate across the whole genome.
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