Methodology article
A factor analysis model for functional genomics
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* Corresponding author: Rafal Kustra r.kustra@utoronto.ca
1 Public Health Sciences, Health Sciences Bldg, University of Toronto, Toronto, ON, Canada
2 Department of Combinatorics and Optimization, University of Waterloo, Waterloo, ON, Canada
3 Department of Statistics and Actuarial Science, Universityof Waterloo, Waterloo, ON, Canada
BMC Bioinformatics 2006, 7:216 doi:10.1186/1471-2105-7-216
Published: 21 April 2006Abstract
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.
Conclusion
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.