Comments

Published in Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, 2005 (Chapter 15, pp. 249-271).

Abstract

The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics. The current version of multtest provides MTPs for tests concerning means, differences in means, and regression parameters in linear and Cox proportional hazards models. Procedures are provided to control Type I error rates defined as tail probabilities for arbitrary functions of the numbers of false positives and rejected hypotheses. These error rates include tail probabilities for the number of false positives (generalized family-wise error rate, gFWER) and the proportion of false positives among the rejected hypotheses (TPPFP). Single-step and step-down common-cut-off (maxT) and common-quantile (minP) procedures, that take into account the joint distribution of the test statistics, are proposed to control the family-wise error rate (FWER), or chance of at least one Type I error. In addition, augmentation multiple testing procedures are provided to control the gFWER and TPPFP, based on any initial FWER-controlling procedure. The results of a multiple testing procedure can be summarized using rejection regions for the test statistics, confidence regions for the parameters of interest, or adjusted p-values. A key ingredient of our proposed MTPs is the test statistics null distribution (and estimator thereof) used to derive rejection regions and corresponding confidence regions and adjusted p-values. Both bootstrap and permutation estimators of the test statistics null distribution are available. The S4 class/method object-oriented programming approach was adopted to summarize the results of a MTP. The modular design of multtest allows interested users to readily extend the package's functionality. Typical testing scenarios are illustrated by applying various MTPs implemented in multtest to the Acute Lymphoblastic Leukemia (ALL) dataset of Chiaretti et al. (2004), with the aim of identifying genes whose expression measures are associated with (possibly censored) biological and clinical outcomes.

Disciplines

Laboratory and Basic Science Research | Multivariate Analysis | Statistical Methodology | Statistical Theory | Survival Analysis