A latent variable model for chemogenomic profiling. Flaherty, P., Giaever, G., Kumm, J., Jordan, M. I, & Arkin, A. P Bioinformatics, 21(15):3286--3293, Aug, 2005. doi abstract bibtex 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.
@article{Flaherty:2005cy,
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.},
Author = {Flaherty, Patrick and Giaever, Guri and Kumm, Jochen and Jordan, Michael I and Arkin, Adam P},
Date-Added = {2015-03-09 17:51:14 +0000},
Date-Modified = {2015-03-09 17:51:21 +0000},
Doi = {10.1093/bioinformatics/bti515},
Journal = {Bioinformatics},
Journal-Full = {Bioinformatics (Oxford, England)},
Mesh = {Computer Simulation; Gene Deletion; Gene Expression Profiling; Gene Expression Regulation, Fungal; Models, Genetic; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pharmaceutical Preparations; Pharmacogenetics; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins},
Month = {Aug},
Number = {15},
Pages = {3286--3293},
Pmid = {15919724},
Pst = {ppublish},
Title = {A latent variable model for chemogenomic profiling},
Volume = {21},
Year = {2005},
Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bti515}}
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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. 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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.\nRESULTS: 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. 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