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

   

Authors:	Zhao, Y.; Chang, C.; Hannum, M.; Lee, J.; Shen, R.
Article Title:	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 9000 tumor samples across canonical oncogenic signaling pathways, immune and stemness phenotype, with comparisons to state-of-the-art clustering methods. We demonstrate that Nebula has the unique advantage of revealing patterns on the basis of shared pathway alterations, offering biological and clinical insights beyond tumor type and histology in the pan-cancer analysis setting. We also illustrate the utility of Nebula in single cell data for immune cell decomposition in peripheral blood samples. © 2021, The Author(s).
Journal Title:	Scientific Reports
Volume:	11
ISSN:	2045-2322
Publisher:	Nature Publishing Group
Date Published:	2021-03-04
Start Page:	5146
Language:	English
DOI:	10.1038/s41598-021-84514-0
PUBMED:	33664338
PROVIDER:	scopus
PMCID:	PMC7933297
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102085449&doi=10.1038%2fs41598-021-84514-0&partnerID=40&md5=32b12832bf0b831c83e5a0ba9cf6162f
Notes:	Article -- Export Date: 1 April 2021 -- Source: Scopus

https://synapse.mskcc.org/synapse/works/has_related_data_chk/178407