Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination betw...

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Main Authors:	Zhang, Shihua, Liu, Chun-Chi, Li, Wenyuan, Shen, Hui, Laird, Peter W., Zhou, Xianghong Jasmine
Format:	Online
Language:	English
Published:	Oxford University Press 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3479191/

id	pubmed-3479191
recordtype	oai_dc
spelling	pubmed-34791912012-10-24 Discovery of multi-dimensional modules by integrative analysis of cancer genomic data Zhang, Shihua Liu, Chun-Chi Li, Wenyuan Shen, Hui Laird, Peter W. Zhou, Xianghong Jasmine Computational Biology Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data. Oxford University Press 2012-10 2012-08-08 /pmc/articles/PMC3479191/ /pubmed/22879375 http://dx.doi.org/10.1093/nar/gks725 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhang, Shihua Liu, Chun-Chi Li, Wenyuan Shen, Hui Laird, Peter W. Zhou, Xianghong Jasmine
spellingShingle	Zhang, Shihua Liu, Chun-Chi Li, Wenyuan Shen, Hui Laird, Peter W. Zhou, Xianghong Jasmine Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
author_facet	Zhang, Shihua Liu, Chun-Chi Li, Wenyuan Shen, Hui Laird, Peter W. Zhou, Xianghong Jasmine
author_sort	Zhang, Shihua
title	Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
title_short	Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
title_full	Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
title_fullStr	Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
title_full_unstemmed	Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
title_sort	discovery of multi-dimensional modules by integrative analysis of cancer genomic data
description	Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data.
publisher	Oxford University Press
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3479191/
_version_	1611918303065800704

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

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