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Bioinformatics Advance Access originally published online on May 26, 2005
Bioinformatics 2005 21(15):3286-3293; doi:10.1093/bioinformatics/bti515
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

A latent variable model for chemogenomic profiling

Patrick Flaherty 1,*, Guri Giaever 3, Jochen Kumm 3, Michael I. Jordan 2 and Adam P. Arkin 4

1Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA
2Division of Computer Science, Department of Statistics, University of California Berkeley, CA 94720, USA
3Stanford Genome Technology Center, Stanford University School of Medicine Palo Alto, CA 94304, USA
4Department of Bioengineering, University of California and Physical Biosciences Division, Lawrence Berkeley National Laboratory, Howard Hughes Medical Institute Berkeley, CA 94720, USA

*To whom correspondence should be addressed.

Motivation: In haploinsufficiency profiling data, pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. We have developed a general probabilistic model that clusters genes and experiments without requiring that a given gene or drug only appear in one cluster. The model also incorporates the functional annotation of known genes to guide the clustering procedure.

Results: We applied our model to the clustering of 79 chemogenomic experiments in yeast. Known pleiotropic genes PDR5 and MAL11 are more accurately represented by the model than by a clustering procedure that requires genes to belong to a single cluster. Drugs such as miconazole and fenpropimorph that have different targets but similar off-target genes are clustered more accurately by the model-based framework. We show that this model is useful for summarizing the relationship among treatments and genes affected by those treatments in a compendium of microarray profiles.

Availability: Supplementary information and computer code at http://genomics.lbl.gov/llda

Contact: flaherty{at}berkeley.edu

Received on July 28, 2004; revised on May 11, 2005; accepted on May 23, 2005

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