Multiple hypothesis testing.- Test statistics null distribution.- Overview of multiple testing procedures.- Single-step multiple testing procedures for controlling general Type I error rates.-Step-down multiple testing procedures for controlling the family-wise error rate.- Augmentation multiple testing procedures for controlling generalized tail probability error rates.- Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability error rates.- Simulation studies: Assessment of test statistics null distributions.- Identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments.- Multiple tests of association with biological annotation metadata.- HIV-1 sequence variation and viral replication capacity.- Genetic mapping of complex human traits using single nucleotide polymorphisms: The ObeLinks Project.- Software implementation.
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
This book provides a detailed account of the theoretical foundations of proposed multiple testing methods and illustrates their application to a range of testing problems in genomics.