Scalable Bayesian Estimation of Multicondition Omics Networks via Clustering and Iterative Merging

Understanding condition-specific molecular interactions in complex biological systems requires scalable and accurate network inference from high-dimensional omics data, often generated by mass spectrometry platforms. Accurately estimating condition-specific interaction networks is critical for highlighting biological mechanisms, yet it remains challenging due to high dimensionality, noise, and the difficulty of accurately comparing networks across experimental groups. We propose a scalable and flexible Bayesian framework, clustering-focused iterative (CFI) estimation, for joint inference of Gaussian graphical models in multicondition omics data. CFI leverages hierarchical clustering of pooled data to identify consistent subnetwork structures, parallel Bayesian estimation within clusters for each condition, and an iterative merging step to recover intercluster dependencies without relying on restrictive assumptions or bridge variables. Through extensive simulations, we demonstrate that CFI achieves substantial computational gains with up to 64% average reduction in runtime relative to running the same network methods without CFI, while maintaining or improving accuracy compared to traditional approaches. The approach is scalable, with demonstrated improvement for large networks with thousands of nodes. We applied CFI to a mass spectrometry-based proteomics data set comparing host responses for samples subjected to mock and SARS-CoV-2 infection. Out of 6721 proteins, CFI identified 576 edges in the mock condition and 589 in the SARS-CoV-2 condition, 159 altered connections involving 63 proteins, reflecting substantial differences in networks between conditions. Enrichment analysis of these differential subnetworks revealed key biological pathways implicated in viral response and host regulation. These results illustrate CFI's ability to scale to realistic omics data, uncover condition-specific network rewiring, and provide interpretable biological insights.