A two-sample test for high-dimensional data with applications to gene-set testing

Abstract

We propose a two-sample test for the means of high-dimensional data when the data dimension is much larger than the sample size. Hotelling's classical $T^2$ test does not work for this "large $p$, small $n$" situation. The proposed test does not require explicit conditions in the relationship between the data dimension and sample size. This offers much flexibility in analyzing high-dimensional data. An application of the proposed test is in testing significance for sets of genes which we demonstrate in an empirical study on a leukemia data set.

Published in at http://dx.doi.org/10.1214/09-AOS716 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (5)

• On testing the significance of sets of genes
• A two-sample test for high-dimensional data with applications to gene-set testing
• Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis
• Multiple tests of association with biological annotation metadata
• To how many simultaneous hypothesis tests can normal, Student's t or bootstrap calibration be applied?

Cited by in corpus (2)

• A two-sample test for high-dimensional data with applications to gene-set testing
• Adaptive post-Dantzig estimation and prediction for non-sparse "large $p$ and small $n$" models