A Distance-Based Test of Association Between Paired Heterogeneous Genomic Data

TL;DR

This paper introduces the generalized RV (GRV) test, a distance-based statistical method for detecting shared patterns of variation across heterogeneous genomic data types, with improved power over traditional tests, demonstrated through simulations and ovarian cancer data.

Contribution

The paper presents the GRV test, a novel distance-based association test that effectively handles heterogeneous data types and offers a closed-form p-value calculation, improving upon the Mantel test.

Findings

GRV test outperforms Mantel test in simulation studies.

Applied to ovarian cancer data, GRV detects pathways with shared genetic and expression variation.

The method enables efficient analysis of diverse biological data types.

Abstract

Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at very different scales or described by very different data structures. We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through distance measures which can be chosen to capture particular aspects of the data. An approximate null distribution is proposed to compute p-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared to the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also report on an application of the GRV test to detect biological pathways in which genetic variability is associated to variation in gene expression levels in ovarian cancer samples, and present results obtained from two independent cohorts.