Gaussian copula correlation network analysis with application to multi-omics data

TL;DR

This paper introduces a Gaussian copula-based method for reconstructing gene regulatory networks from multi-omics data, effectively handling mixed data types and providing a new tool for biological network analysis.

Contribution

It presents a novel Gaussian copula approach with a semiparametric estimation method for mixed data, validated through simulations and applied to breast cancer data.

Findings

Accurately estimates copula correlation matrices in simulations

Effectively reconstructs gene networks from real multi-omics data

Provides an accessible R package for implementation

Abstract

Reconstructing gene regulatory networks from large-scale heterogeneous data is a key challenge in biology. In multi-omics data analysis, networks based on pairwise statistical association measures remain popular, as they are easy to build and understand. In the presence of mixed-type (discrete and continuous) data, however, the choice of good association measures remains an important issue. We propose here a novel approach based on the Gaussian copula, the parameters of which represent the links of the network. Novel properties of the model are obtained to guide the interpretation of the network. To estimate the copula parameters, we calculated a semiparametric pairwise likelihood for mixed data. In an extensive simulation study, we showed that the proposed estimation procedure was able to accurately estimate the copula correlation matrix. The proposed methodology was also applied to a real ICGC dataset on breast cancer, and is implemented in a freely available R package heterocop.