Multiple Testing Procedures with Applications to Genomics
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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.
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
From the reviews:
"This book summarizes the recent work of Sandrine Dudoit and Mark van der Laan on multiple testing. It proposes a general framework for multiple testing procedures (MTPs) and introduces new concepts ... . The authors also provide code for reproducing the results of some of the applications. ... if one is looking for a detailed summary of the latest developments in multiple testing regarding MTPs or in the application of MTPs to biomedical and genomic data, then this book is an excellent reference." (Holger Schwender, Statistical Papers, Vol. 50, 2009)
"In the last decade a growing amount of statistical research has been devoted to multiple testing. This book summarizes the recent work on this area. ... very useful for the applied researcher who would like to understand how to apply multiple testing. ... a good reference for statisticians interested in a general treatment of multiple testing." (Avner Bar-Hen, Mathematical Reviews, Issue 2009 j)
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.