Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion

TL;DR

This paper introduces a multivariate sparse fusion method to analyze heterogeneity in genetic regulations across subgroups, guided by biomarkers, addressing high-dimensional challenges in complex disease studies.

Contribution

The paper proposes a novel penalized fusion approach for identifying subgroup structures and regulation relationships in high-dimensional genetic data, guided by known biomarkers.

Findings

Identified distinct genetic regulation patterns in melanoma and stomach cancer.

Demonstrated the effectiveness of the method through extensive simulations.

Applied the approach to TCGA data revealing biologically relevant heterogeneity.

Abstract

Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene expressions) by CNVs (copy number variations) and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation as well as sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is "guided" by a known biomarker. We develop an MSF (Multivariate Sparse Fusion) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA data leads to interesting findings.