Bagging multiple comparisons from microarray data

TL;DR

This paper explores the use of bagging and subagging techniques to enhance the power of large-scale hypothesis testing in microarray data, demonstrating improved discovery rates with controlled false discoveries.

Contribution

It introduces bagging and subagging methods tailored for multiple hypothesis testing, showing their effectiveness in real and simulated microarray datasets.

Findings

Bagging and subagging improve test power.

Subagging with maximum contrast reduces false discovery rate.

Methods perform well in both simulated and real data.

Abstract

The problem of large-scale simultaneous hypothesis testing is re-visited. Bagging and subagging procedures are put forth with the purpose of improving the discovery power of the tests. The procedures are implemented in both simulated and real data. It is shown that bagging and subagging significantly improve power at the cost of a small increase in false discovery rate with the proposed `maximum contrast' subagging having an edge over bagging, i.e., yielding similar power but significantly smaller false discovery rates.