Multi-omics network inference with a Gaussian copula model

BACKGROUND: Inferring partial correlation networks is essential in systems biology to uncover direct interactions between biological entities. Traditional Gaussian graphical models rely on the assumption of normally distributed data; this assumption is not satisfied when dealing with multi-omics datasets comprising heterogeneous data types such as continuous and discrete variables. RESULTS: We propose a novel likelihood-based approach for network inference using a Gaussian copula model with semiparametric pairwise-likelihood estimation of the latent correlation matrix. The inferred correlation structure is then inverted and regularized via the graphical lasso to recover latent partial correlations. Compared to a moment-based approach employing bridge functions, our method demonstrates significantly improved computational efficiency and estimation accuracy, particularly for discrete data with many categories and/or large values, such as count data. This result is important for biological applications, especially for the integration of RNA-seq count data. An application to a breast cancer data set from the International Cancer Genome Consortium (ICGC) successfully identified biologically relevant interactions. CONCLUSIONS: The proposed approach, based on the Gaussian copula and likelihood-based estimation, provides a novel, effective and computationally efficient mathematical framework for integrative multi-omics data analysis and network inference.