Importance of presenting the variability of the false discovery rate control
Journal
BMC Genetics
Journal Volume
16
Journal Issue
1
Date Issued
2015
Author(s)
Lin Y.-T.
Abstract
Background: 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. Results: 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. Conclusions: We advise researchers to present the bootstrapped standard errors alongside with the false discovery rate indices. ? 2015 Lin and Lee.
SDGs
Other Subjects
Article; bootstrapping; colon cancer; data analysis; false discovery rate control; gene expression; genetic association; genetic procedures; genome analysis; genomics; refraction error; algorithm; colon tumor; genetics; genome-wide association study; human; procedures; statistical model; Algorithms; Colonic Neoplasms; Genome-Wide Association Study; Humans; Models, Statistical
Publisher
BioMed Central Ltd.
Type
journal article