title: Network-guided regression for detecting associations between DNA methylation and gene expression.
abstract: BACKGROUND
  High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. These data could yield discoveries that may lead to breakthroughs in the diagnosis and treatment of human disease, but require statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.
  
  
  RESULTS
  We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to The Cancer Genome Atlas datasets on primary ovarian tumours.
  
  
  METHODS
  R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/∼gmontana
  
  
  BACKGROUND
  giovanni.montana@kcl.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, King's College London, St Thomas' Hospital, London SE1 7EH, UK.
authors.forename: Zi
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, King's College London, St Thomas' Hospital, London SE1 7EH, UK.
authors.forename: Edward
authors.initials: E
authors.lastname: Curry
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, King's College London, St Thomas' Hospital, London SE1 7EH, UK Department of Mathematics, Imperial College London, London SW7 2AZ, Division of Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN and Department of Biomedical Engineering, King's College London, St Thomas' Hospital, London SE1 7EH, UK.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Montana
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation; D004843:Epistasis, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms; D012044:Regression Analysis