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

Zhao, Yize ; Chang, Changgee ; Hannum, Margaret ; Lee, Jasme ; Shen, Ronglai

Scientific reports, 2021-03, Vol.11 (1), p.5146-11, Article 5146 [Revista revisada por pares]

England: Nature Publishing Group

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Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
Autor: Zhao, Yize ; Chang, Changgee ; Hannum, Margaret ; Lee, Jasme ; Shen, Ronglai
Materias: Bayes Theorem ; Bayesian analysis ; Carcinogenesis - genetics ; Cluster Analysis ; Clustering ; Computational Biology - statistics & numerical data ; Computer applications ; Epigenomics ; Genomic analysis ; Genomics ; Genomics - statistics & numerical data ; Humans ; Models, Statistical ; Neoplasms - genetics ; Neoplasms - pathology ; Peripheral blood ; Phenotypes ; Transcriptome - genetics
Es parte de: Scientific reports, 2021-03, Vol.11 (1), p.5146-11, Article 5146
Descripción: 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.
Editor: England: Nature Publishing Group
Idioma: Inglés

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

Zhao, Yize ; Chang, Changgee ; Hannum, Margaret ; Lee, Jasme ; Shen, Ronglai

England: Nature Publishing Group

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