title: Evaluation of hierarchical models for integrative genomic analyses.
abstract: Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.
  
  
  
  Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival.
  
  
  
  Source code implementing the integrative models is freely available at https://github.com/mgt000/IntegrativeAnalysis along with example datasets and sample R script applying the models to these data. The TCGA datasets used for analysis are publicly available at https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp
  
  
  
  marie.denis@cirad.fr or mgt26@georgetown.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: UMR AGAP, CIRAD, Montpellier, France, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA and.
authors.forename: Marie
authors.initials: M
authors.lastname: Denis
authors.affiliation: Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA.
authors.forename: Mahlet G
authors.initials: MG
authors.lastname: Tadesse
mesh_terms: D000465:Algorithms; D016678:Genome; D023281:Genomics; D005909:Glioblastoma; D006801:Humans; D016013:Likelihood Functions