Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data.

ABSTRACT: Multi-modal molecular profiling data in bulk tumors or single cells are accumulating at a fast pace. There is a great need for developing statistical and computational methods to reveal molecular structures in complex data types toward biological discoveries. Here, we introduce Nebula, a novel Bayesian integrative clustering analysis for high dimensional multi-modal molecular data to identify directly interpretable clusters and associated biomarkers in a unified and biologically plausible framework. To facilitate computational efficiency, a variational Bayes approach is developed to approximate the joint posterior distribution to achieve model inference in high-dimensional settings. We describe a pan-cancer data analysis of genomic, epigenomic, and transcriptomic alterations in close to 90

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC7933297 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature