A latent Variable Model for Chemogenomic Profiling

Publication Date

August 2005

Journal or Book Title



Motivation: In haploinsufficiency profiling data, pleiotropic genesare often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. Wehave developed a general probabilistic model that clusters genes andexperiments without requiring that a given gene or drug only appearin one cluster. The model also incorporates the functional annotationof known genes to guide the clustering procedure.Results: We applied our model to the clustering of 79 chemogenomicexperiments in yeast. Known pleiotropic genes PDR5 and MAL11 aremore 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.