Using prior information to boost power in correlation structure support recovery

TL;DR

This paper introduces a FAB procedure that leverages prior information to enhance the power of correlation structure testing in high-dimensional data, demonstrating significant gains in genomic studies.

Contribution

It develops a new FAB approach incorporating prior data or hierarchical models to improve correlation testing power in high-dimensional settings.

Findings

Significant power improvements in gene correlation detection.

Maintains control of Type I error and FDR.

Efficient divide-and-conquer algorithm for large-scale testing.

Abstract

Hypothesis testing of structure in correlation and covariance matrices is of broad interest in many application areas. In high dimensions and/or small to moderate sample sizes, high error rates in testing is a substantial concern. This article focuses on increasing power through a frequentist assisted by Bayes (FAB) procedure. This FAB approach boosts power by including prior information on the correlation parameters. In particular, we suppose there is one of two sources of prior information: (i) a prior dataset that is distinct from the current data but related enough that it may contain valuable information about the correlation structure in the current data; and (ii) knowledge about a tendency for the correlations in different parameters to be similar so that it is appropriate to consider a hierarchical model. When the prior information is relevant, the proposed FAB approach can have significant gains in power. A divide-and-conquer algorithm is developed to reduce computational complexity in massive testing dimensions. We show improvements in power for detecting correlated gene pairs in genomic studies while maintaining control of Type I error or false discover rate (FDR).