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

TL;DR

This paper introduces a new two-sample test designed for high-dimensional data where traditional methods fail, providing a flexible approach for gene-set significance testing in genomics.

Contribution

It proposes a novel two-sample test that works without strict conditions on data dimension and sample size, suitable for high-dimensional gene-set analysis.

Findings

Effective in high-dimensional gene-set testing

Applicable to leukemia gene expression data

Does not require explicit p-n relationship

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.