ArticleComputational and mathematical methods in medicine2013
Power and stability properties of resampling-based multiple testing procedures with applications to gene oncology studies.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Differences in Alternative Splicing between Yellow and Black-Seeded Rapeseed.Plants (Basel, Switzerland) · 2020Article
- Methods to adjust for multiple comparisons in the analysis and sample size calculation of randomised controlled trials with multiple primary outcomes.BMC medical research methodology · 2019Article
- Article
- Weighted gene co‑expression network analysis for identifying hub genes in association with prognosis in Wilms tumor.Molecular medicine reports · 2019Article
- Familiarity with social sounds alters c-Fos expression in auditory cortex and interacts with estradiol in locus coeruleus.Hearing research · 2018Article
- Competing endogenous RNA regulatory network in papillary thyroid carcinoma.Molecular medicine reports · 2018Article
- Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies.BMC medical research methodology · 2017Article
- An evaluation of statistical methods for DNA methylation microarray data analysis.BMC bioinformatics · 2015Article
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2 authors.
Funding
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.
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