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 minPprocedure and the bootstrap step-down minPprocedure 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 maxTprocedure and the permutation minPprocedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.
Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis
