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Bioinformatics Advance Access published online on May 26, 2005
Bioinformatics, 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@oupjournals.org
Received July 28, 2004
Revised May 11, 2005
Accepted May 23, 2005

Article

A latent variable model for chemogenomic profiling

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

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

* To whom correspondence should be addressed.
Patrick Flaherty, E-mail: flaherty{at}berkeley.edu


  Abstract

Motivation: In haploinsufficiency profiling (HIP) data (Giaever et al., 2002), 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/~patrickf/llda.html.


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