Isaac Dialsingh1, Stefanie R Austin2, Naomi S Altman2. 1. Department of Mathematics and Statistics, The University of the West Indies, St. Augustine Campus, Trinidad and Tobago and. 2. Department of Statistics, The Pennsylvania State University, State College, PA 16802-2111, USA.
Abstract
MOTIVATION: In high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems. RESULTS: This article introduces a number of π0 estimators, the regression and 'T' methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data. AVAILABILITY AND IMPLEMENTATION: implemented in R.
MOTIVATION: In high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems. RESULTS: This article introduces a number of π0 estimators, the regression and 'T' methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data. AVAILABILITY AND IMPLEMENTATION: implemented in R.
Authors: Logan C Walker; Louise Marquart; John F Pearson; George A R Wiggins; Tracy A O'Mara; Michael T Parsons; Daniel Barrowdale; Lesley McGuffog; Joe Dennis; Javier Benitez; Thomas P Slavin; Paolo Radice; Debra Frost; Andrew K Godwin; Alfons Meindl; Rita Katharina Schmutzler; Claudine Isaacs; Beth N Peshkin; Trinidad Caldes; Frans Bl Hogervorst; Conxi Lazaro; Anna Jakubowska; Marco Montagna; Xiaoqing Chen; Kenneth Offit; Peter J Hulick; Irene L Andrulis; Annika Lindblom; Robert L Nussbaum; Katherine L Nathanson; Georgia Chenevix-Trench; Antonis C Antoniou; Fergus J Couch; Amanda B Spurdle Journal: Eur J Hum Genet Date: 2017-02-01 Impact factor: 4.246
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