title: Hypothesis testing at the extremes: fast and robust association for high-throughput data.
abstract: A number of biomedical problems require performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared with the doubled [Formula: see text]-value, due to asymmetry in the null distribution. Exact (permutation) testing is attractive, but can be computationally intensive and cumbersome. We present an approximation to exact association tests of trend that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled [Formula: see text]-values. The approach is shown to be equivalent under permutation to likelihood ratio tests for the most commonly used generalized linear models (GLMs). For linear regression, covariates are handled by working with covariate-residualized responses and predictors. For GLMs, stratified covariates can be handled in a manner similar to exact conditional testing. Simulations and examples illustrate the wide applicability of the approach. The accompanying mcc package is available on CRAN http://cran.r-project.org/web/packages/mcc/index.html.
authors: Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA yihui_zhou@ncsu.edu.
authors: Yi-Hui
authors: YH
authors: Zhou
authors: Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
authors: Fred A
authors: FA
authors: Wright
mesh_terms: D056808:Biostatistics; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D003550:Cystic Fibrosis / Q000235:genetics; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D005260:Female; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D020641:Polymorphism, Single Nucleotide; D018401:Sample Size; D012984:Software
keywords: Density approximation; Exact testing; Permutation