Brian Segal, Biostatistics, University of Michigan

Graduate Student Seminar

Abstract:
Researchers in genetics and other life sciences commonly use permutation tests to evaluate differences between groups. Permutation tests have desirable properties, including exactness, and are applicable even when the distribution of the test statistic is analytically intractable. However, permutation tests can also be computationally intensive.

To address this computational challenge, we propose both an asymptotic approximation and a resampling algorithm for quickly estimating small permutation p-values (e.g. < 10^−6 ) for functions of the means in two-sample tests. We will present our methods and demonstrate their use through simulations and an application to cancer genomic data. Through simulations, we find that our resampling algorithm is more computationally efficient than another leading alternative, particularly or extremely small p-values (e.g. < 10^−30). Through application to cancer genomic data, we find that our methods can successfully identify up- and down-regulated genes.