Selective testing and its effect on false discovery rate controlling procedures under discrete framework.

AbstractThe support of the <italic>p</italic>-value corresponding to a discrete test-statistic is a finite isolated subset of the unit interval. In differential gene expression studies with RNA-sequence datasets, multiple discrete tests are performed. The objective of such studies are to identify differentially expressed genes with a control over a measure of false discoveries. We suggest to remove hypotheses corresponding to the <italic>p</italic>-values having minimum support-point greater than the threshold of that overall measure. We argue this suggestion and present methods of filtering the set of hypotheses for two useful testing scenarios. Simulation experiments show gain in power of the multiple testing procedures when applied to the filtered set of hypotheses. We also demonstrate the method with two real datasets, related to the study of HIV and mammary gland. [ABSTRACT FROM AUTHOR]