StATS: A totally negative microarray experiment (October 14, 2005).

I've been cleaning out my old emails and am finding some real gems of good advice. Someone wrote into the Bioconductor email list wondering what to do when the lowest adjusted p-value in the entire experiment was still very large (0.66). A nice response outlined three strategies:

  1. Filter the genes prior to statistical analysis. There are a variety of filters that people have used. With a smaller list of genes to start from, the adjustments to the p-values are less severe.
  2. Select a set of genes that you suspect a priori to be differentially expressed. You might select, for example, all genes that belong to a particular GO (Gene Ontology) category.
  3. Ignore the adjusted p-values and just select the 10, 25, or however many genes with the smallest unadjusted p-values. These may not be statistically significant, but they still may be worth following through with using quantitative PCR or another confirmatory approach. If the quantitative PCR is positive, you have validation outside the microarray experiment that these genes are indeed differentially expressed.

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