TY - JOUR
T1 - Integrative network-based Bayesian analysis of diverse genomics data
AU - Wang, Wenting
AU - Baladandayuthapani, Veerabhadran
AU - Holmes, Chris C.
AU - Do, Kim Anh
N1 - Funding Information:
We thank Virginia Mohlere for editing the manuscript. The research described was partially supported by the Cancer Center Support Grant (CCSG) (P30 CA016672). KAD is partially supported by the MD Anderson Cancer Center SPORE grants in Brain Cancer (P50 CA127001 03), in Breast Cancer (P50) CA116199), and in Prostate Cancer (P50 CA140388 02). VB research is partially supported by NIH grant R01 CA160736. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
PY - 2013/10/1
Y1 - 2013/10/1
N2 - Background: In order to better understand cancer as a complex disease with multiple genetic and epigenetic factors, it is vital to model the fundamental biological relationships among these alterations as well as their relationships with important clinical outcomes.Methods: We develop an integrative network-based Bayesian analysis (iNET) approach that allows us to jointly analyze multi-platform high-dimensional genomic data in a computationally efficient manner. The iNET approach is formulated as an objective Bayesian model selection problem for Gaussian graphical models to model joint dependencies among platform-specific features using known biological mechanisms. Using both simulated datasets and a glioblastoma (GBM) study from The Cancer Genome Atlas (TCGA), we illustrate the iNET approach via integrating three data types, microRNA, gene expression (mRNA), and patient survival time.Results: We show that the iNET approach has greater power in identifying cancer-related microRNAs than non-integrative approaches based on realistic simulated datasets. In the TCGA GBM study, we found many mRNA-microRNA pairs and microRNAs that are associated with patient survival time, with some of these associations identified in previous studies.Conclusions: The iNET discovers relationships consistent with the underlying biological mechanisms among these variables, as well as identifying important biomarkers that are potentially relevant to patient survival. In addition, we identified some microRNAs that can potentially affect patient survival which are missed by non-integrative approaches.
AB - Background: In order to better understand cancer as a complex disease with multiple genetic and epigenetic factors, it is vital to model the fundamental biological relationships among these alterations as well as their relationships with important clinical outcomes.Methods: We develop an integrative network-based Bayesian analysis (iNET) approach that allows us to jointly analyze multi-platform high-dimensional genomic data in a computationally efficient manner. The iNET approach is formulated as an objective Bayesian model selection problem for Gaussian graphical models to model joint dependencies among platform-specific features using known biological mechanisms. Using both simulated datasets and a glioblastoma (GBM) study from The Cancer Genome Atlas (TCGA), we illustrate the iNET approach via integrating three data types, microRNA, gene expression (mRNA), and patient survival time.Results: We show that the iNET approach has greater power in identifying cancer-related microRNAs than non-integrative approaches based on realistic simulated datasets. In the TCGA GBM study, we found many mRNA-microRNA pairs and microRNAs that are associated with patient survival time, with some of these associations identified in previous studies.Conclusions: The iNET discovers relationships consistent with the underlying biological mechanisms among these variables, as well as identifying important biomarkers that are potentially relevant to patient survival. In addition, we identified some microRNAs that can potentially affect patient survival which are missed by non-integrative approaches.
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U2 - 10.1186/1471-2105-14-S13-S8
DO - 10.1186/1471-2105-14-S13-S8
M3 - Article
C2 - 24267288
AN - SCOPUS:84886860465
SN - 1471-2105
VL - 14
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - SUPPL13
M1 - S8
ER -