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 no...

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Autores principales:	Yize Zhao, Changgee Chang, Margaret Hannum, Jasme Lee, Ronglai Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b852817d72b488da8b80254eba51485
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spelling	oai:doaj.org-article:3b852817d72b488da8b80254eba514852021-12-02T13:34:51ZBayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data10.1038/s41598-021-84514-02045-2322https://doaj.org/article/3b852817d72b488da8b80254eba514852021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84514-0https://doaj.org/toc/2045-2322Abstract 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.Yize ZhaoChanggee ChangMargaret HannumJasme LeeRonglai ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yize Zhao Changgee Chang Margaret Hannum Jasme Lee Ronglai Shen Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
description	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.
format	article
author	Yize Zhao Changgee Chang Margaret Hannum Jasme Lee Ronglai Shen
author_facet	Yize Zhao Changgee Chang Margaret Hannum Jasme Lee Ronglai Shen
author_sort	Yize Zhao
title	Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
title_short	Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
title_full	Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
title_fullStr	Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
title_full_unstemmed	Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
title_sort	bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3b852817d72b488da8b80254eba51485
work_keys_str_mv	AT yizezhao bayesiannetworkdrivenclusteringanalysiswithfeatureselectionforhighdimensionalmultimodalmoleculardata AT changgeechang bayesiannetworkdrivenclusteringanalysiswithfeatureselectionforhighdimensionalmultimodalmoleculardata AT margarethannum bayesiannetworkdrivenclusteringanalysiswithfeatureselectionforhighdimensionalmultimodalmoleculardata AT jasmelee bayesiannetworkdrivenclusteringanalysiswithfeatureselectionforhighdimensionalmultimodalmoleculardata AT ronglaishen bayesiannetworkdrivenclusteringanalysiswithfeatureselectionforhighdimensionalmultimodalmoleculardata
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Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data

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