Differential expression and network inferences through functional data modeling

Donatello Telesca, Lurdes Y.T. Inoue, Mauricio Neira, Ruth Etzioni, Martin Gleave, Colleen Nelson

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene-expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene-specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time-transformation functions allow us to examine gene networks while accounting for features of the gene-expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.

Original languageEnglish (US)
Pages (from-to)793-804
Number of pages12
JournalBiometrics
Volume65
Issue number3
DOIs
StatePublished - Sep 2009

Keywords

  • Bayesian hierarchical model
  • Differential expression
  • Functional data
  • Functional similarity
  • Gene networks
  • Time course microarray data
  • Time transformation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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