Resampling-Based Multiple Hypothesis Testing with Applications to Genomics: New Developments in the R/Bioconductor Package multtest

摘要

The multtest package is a standard Bioconductor package containing a suite of functions useful for executing, summarizing, and displaying the results from a wide variety of multiple testing procedures (MTPs). In addition to many popular MTPs, the central methodological focus of the multtest package is the implementation of powerful joint multiple testing procedures. Joint MTPs are able to account for the dependencies between test statistics by effectively making use of (estimates of) the test statistics joint null distribution. To this end, two additional bootstrap-based estimates of the test statistics joint null distribution have been developed for use in the package. For asymptotically linear estimators involving single-parameter hypotheses (such as tests of means, regression parameters, and correlation parameters using t-statistics), a computationally efficient joint null distribution estimate based on influence curves is now also available. New MTPs implemented in multtest include marginal adaptive procedures for control of the false discovery rate (FDR) as well as empirical Bayes joint MTPs which can control any Type I error rate defined as a function of the numbers of false positives and true positives. Examples of such error rates include, among others, the family-wise error rate and the FDR. S4 methods are available for objects of the new class EBMTP, and particular attention has been given to reducing the need for repeated resampling between function calls.