This paper presents an overview of the current state of the art in multiple testing in genomics
data from a user's perspective. We describe methods for familywise error control, false …

In the last decade a growing amount of statistical research has been devoted to multiple
testing, motivated by a variety of applications in medicine, bioinformatics, genomics, brain …

This book establishes the theoretical foundations of a general methodology for multiple
hypothesis testing and discusses its software implementation in R and SAS. The methods …

The present article proposes general single-step multiple testing procedures for controlling
Type I error rates defined as arbitrary parameters of the distribution of the number of Type I …

Motivation: Multiple hypothesis testing is a common problem in genome research,
particularly in microarray experiments and genomewide association studies. Failure to …

Genomic methods have made statistical multiple-test methods important to geneticists and
molecular biologists. These tests apply to identification of quantitative trait loci and …

In the context of large-scale multiple hypothesis testing, the hypotheses often possess
certain group structures based on additional information such as Gene Ontology in gene …

Scientists from nearly all disciplines face the problem of simultaneously evaluating many
hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible …

Testing for significance with gene expression data from DNA microarray experiments
involves simultaneous comparisons of hundreds or thousands of genes. If R denotes the …

The burgeoning field of genomics has revived interest in multiple testing procedures by
raising new methodological and computational challenges. For example, microarray …