Importance of presenting the variability of the false discovery rate control

Multiple hypothesis testing is a pervasive problem in genomic data analysis. The conventional Bonferroni method which controls the family-wise error rate is conservative and with low power. The current paradigm is to control the false discovery rate.

We characterize the variability of the false discovery rate indices (local false discovery rates, q-value and false discovery proportion) using the bootstrapped method. A colon cancer gene-expression data and a visual refractive errors genome-wide association study data are analyzed as demonstration. We found a high variability in false discovery rate controls for typical genomic studies.

We advise researchers to present the bootstrapped standard errors alongside with the false discovery rate indices.