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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 min P  procedure and the bootstrap step-down min P  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 max T  procedure and the permutation min P  procedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.

Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies