Bayesian Networks for Omics Data Analysis in Hepatocellular Carcinoma Single-Cell Sequencing

Abstract

Single cell multi omics techniques have shown an advancement in unrevealing complex diseases like cancer heterogeneity by providing multi-faceted insight into their individual cellular regulations. In this study, a machine learning approach, Bayesian network (BN), has been applied to integrate genomic, epigenomic, and transcriptomic data in hepatocellular carcinoma at single cell resolution. Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high metastatic rate and reckoned for poor prognosis. Heterogeneity of tumor cells is concerned with cancer progression, metastasis, therapeutic resistance, and mortality. For this purpose, a dataset from a published study of 25 single cell sequencing of hepatocellular carcinoma were used. First, DNA methylome and transcriptome data were analyzed on their own. Copy number variations were estimated from DNA methylome data by using the Hidden Markov Model method. To reveal the causal relationship between the omics, three BN models were constructed. The models were fitted to their parameters by using maximum likelihood estimation. For model evaluation, score-based criteria, Akaike information criterion and Bayesian information criterion, were used. 207 genes with significant models have been detected. The heterogeneity of the omics and their regulation mechanisms with each other have been shown, by pointing to genes that follow different BN models that take place in major pathways in HCC.