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Bioinformatics. 2005 Aug 1;21(15):3286-93. Epub 2005 May 26.

A latent variable model for chemogenomic profiling.

Author information

  • 1Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu

Abstract

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.

PMID:
15919724
[PubMed - indexed for MEDLINE]
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