Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies. (20th November 2013)

• Record Type: Journal Article
• Authors: Li, Dongmei; Dye, Timothy D.
• Other Names: Yuan Ao Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2013(2013)
• Publication Date: 2013-11-20
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2013/610297
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations and gene oncology examples. The bootstrap single-step minP procedure and the bootstrap step-down minP procedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation maxT procedure and the permutation minP procedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.