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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.

Multiple Testing Procedures with Applications to Genomics

Multiple Hypothesis Testing.- Test Statistics Null Distribution.- Overview of Multiple Testing Procedures.- Single-Step Multiple Testing Procedures for Controlling General Type I Error Rates, ?(Fvn).- 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.