title: A factor analysis model for functional genomics.
abstract: BACKGROUND
  Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories.
  
  
  RESULTS
  We propose a factor analysis model (FAM) for functional genomics and give a two-step algorithm, using genome-wide expression data for yeast and a subset of Gene-Ontology Biological Process functional annotations. We show that the predictive performance of our method is comparable to the current best approach while our total computation time was faster by a factor of 4000. We discuss the unique challenges in performance evaluation of algorithms used for genome-wide functions genomics. Finally, we discuss extensions to our method that can incorporate the inherent correlation structure of the functional categories to further improve predictive performance.
  
  
  CONCLUSIONS
  Our factor analysis model is a computationally efficient technique for functional genomics and provides a clear and unified statistical framework with potential for incorporating important gene ontology information to improve predictions.
authors.affiliation: Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca
authors.forename: Rafal
authors.initials: R
authors.lastname: Kustra
authors.forename: Romy
authors.initials: R
authors.lastname: Shioda
authors.forename: Mu
authors.initials: M
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins