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Identifying key multifunctional components shared by critical cancer and normal liver pathways via sparseGMM

Despite the abundance of multi-modal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here we present SparseGMM, a novel statistical approach for gene regulatory network discovery. SparseGMM uniquely uses latent variable modeling with sparsity constraints regulators to learn gaussian mixtures from multi-omic data. By combining co-expression patterns with a Bayesian framework, sparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate the discovered gene modules in an independent scRNA-seq dataset. sparseGMM identifies PROCR as a regulator of angiogenesis, and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer, respectively. Additionally, we show that more genes have significantly higher entropy in cancer compared to normal liver; among high entropy genes are key multifunctional components shared by critical pathways, such as p53 and estrogen signaling.