title: Joint network and node selection for pathway-based genomic data analysis.
abstract: BACKGROUND
  By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.
  
  
  RESULTS
  In this article, we propose a novel sparse Bayesian model for joint network and node selection. This model integrates information from networks (e.g. pathways) and nodes (e.g. genes) by a hybrid of conditional and generative components. For the conditional component, we propose a sparse prior based on graph Laplacian matrices, each of which encodes detailed correlation structures between network nodes. For the generative component, we use a spike and slab prior over network nodes. The integration of these two components, coupled with efficient variational inference, enables the selection of networks as well as correlated network nodes in the selected networks. Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods. Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature. In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.
  
  
  BACKGROUND
  The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html.
  
  
  BACKGROUND
  alanqi@purdue.edu.
authors.affiliation: Department of Computer Science, Department of Biology, and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Shandian
authors.initials: S
authors.lastname: Zhe
authors.forename: Syed A Z
authors.initials: SA
authors.lastname: Naqvi
authors.forename: Yifan
authors.initials: Y
authors.lastname: Yang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D021441:Carcinoma, Pancreatic Ductal / Q000235:genetics / Q000378:metabolism; D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism; D016208:Databases, Factual; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics / Q000378:metabolism; D010190:Pancreatic Neoplasms / Q000235:genetics / Q000378:metabolism