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iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data
Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546799/ https://www.ncbi.nlm.nih.gov/pubmed/23142963 http://dx.doi.org/10.1093/bioinformatics/bts655 |
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author | Wang, Wenting Baladandayuthapani, Veerabhadran Morris, Jeffrey S. Broom, Bradley M. Manyam, Ganiraju Do, Kim-Anh |
author_facet | Wang, Wenting Baladandayuthapani, Veerabhadran Morris, Jeffrey S. Broom, Bradley M. Manyam, Ganiraju Do, Kim-Anh |
author_sort | Wang, Wenting |
collection | PubMed |
description | Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3546799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-35467992013-01-16 iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data Wang, Wenting Baladandayuthapani, Veerabhadran Morris, Jeffrey S. Broom, Bradley M. Manyam, Ganiraju Do, Kim-Anh Bioinformatics Original Papers Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-01-15 2012-11-09 /pmc/articles/PMC3546799/ /pubmed/23142963 http://dx.doi.org/10.1093/bioinformatics/bts655 Text en © The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Wang, Wenting Baladandayuthapani, Veerabhadran Morris, Jeffrey S. Broom, Bradley M. Manyam, Ganiraju Do, Kim-Anh iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data |
title | iBAG: integrative Bayesian analysis of high-dimensional multiplatform
genomics data |
title_full | iBAG: integrative Bayesian analysis of high-dimensional multiplatform
genomics data |
title_fullStr | iBAG: integrative Bayesian analysis of high-dimensional multiplatform
genomics data |
title_full_unstemmed | iBAG: integrative Bayesian analysis of high-dimensional multiplatform
genomics data |
title_short | iBAG: integrative Bayesian analysis of high-dimensional multiplatform
genomics data |
title_sort | ibag: integrative bayesian analysis of high-dimensional multiplatform
genomics data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546799/ https://www.ncbi.nlm.nih.gov/pubmed/23142963 http://dx.doi.org/10.1093/bioinformatics/bts655 |
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