A mixture model for the evolution of gene expression in non-homogeneous datasets

Gerald Quon, Yee Whye Teh, Esther Chan, Timothy Hughes, Michael Brudno, Quaid Morris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest in species such as human and mouse. We present Brownian Factor Phylogenetic Analysis, a statistical model that makes a number of significant extensions to previous models to enable characterization of changes in expression among highly complex organisms. We demonstrate the efficacy of our method on a microarray dataset profiling diverse tissues from multiple vertebrate species. We anticipate that the model will be invaluable in the study of gene expression patterns in other diverse organisms as well, such as worms and insects.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages1297-1304
Number of pages8
Publication statusPublished - Dec 1 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Other

Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

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ASJC Scopus subject areas

  • Information Systems

Cite this

Quon, G., Teh, Y. W., Chan, E., Hughes, T., Brudno, M., & Morris, Q. (2009). A mixture model for the evolution of gene expression in non-homogeneous datasets. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 1297-1304)