BONOBO: Bayesian Optimized Sample-Specific Networks Obtained by Omics Data

Correlation networks can provide important insights into biological systems by uncovering intricate interactions between genes and their molecular regulators. However, methods for estimating co-expression networks generally derive an aggregate population-specific network that represents the mean regulatory properties of the entire population and hence falls short in capturing heterogeneity across individuals. While numerous methods have been proposed to estimate sample-specific co-expression networks, they fail to estimate positive semidefinite correlation networks and, hence, are subject to misinterpretation. To fill this gap in co-expression network inference, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by acknowledging heterogeneity in molecular interactions across individuals. For each sample, BONOBO imposes a Gaussian distribution on the log-transformed, centered gene expression and a conjugate Inverse Wishart prior distribution on the sample-specific co-expression matrix constructed from assimilating all other samples in the data. BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices by combining the sample-specific gene expression with the prior distribution. We demonstrate the advantages of BONOBO using several simulated and real datasets. BONOBO is computationally scalable and available as open-source software through the Network Zoo package (from netZooPy v0.10.0; netzoo.github.io). A preprint associated with this abstract can be found on bioRxiv (doi: 10.1101/2023.11.16.567119v1).