A latent Variable Model for Chemogenomic ProfilingBioinformatics (2005)
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.
Publication DateSummer August 1, 2005
Citation InformationPatrick Flaherty. "A latent Variable Model for Chemogenomic Profiling" Bioinformatics Vol. 21 Iss. 15 (2005) p. 3286 - 3293
Available at: http://works.bepress.com/patrick-flaherty/3/