title: iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://odin.mdacc.tmc.edu/∼vbaladan/.
  
  
  BACKGROUND
  veera@mdanderson.org
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Wenting
authors.initials: W
authors.lastname: Wang
authors.forename: Veerabhadran
authors.initials: V
authors.lastname: Baladandayuthapani
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Morris
authors.forename: Bradley M
authors.initials: BM
authors.lastname: Broom
authors.forename: Ganiraju
authors.initials: G
authors.lastname: Manyam
authors.forename: Kim-Anh
authors.initials: KA
authors.lastname: Do
mesh_terms: D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000401:mortality*; D019175:DNA Methylation; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods; D005909:Glioblastoma / Q000235:genetics* / Q000378:metabolism / Q000401:mortality*; D006801:Humans; D015233:Models, Statistical