title: Recommending pathway genes using a compendium of clustering solutions.
abstract: A common approach for identifying pathways from gene expression data is to cluster the genes without using prior information about a pathway, which often identifies only the dominant coexpression groups. Recommender systems are well-suited for using the known genes of a pathway to identify the appropriate experiments for predicting new members. However, existing systems, such as the GeneRecommender, ignore how genes naturally group together within specific experiments. We present a collaborative filtering approach which uses the pattern of how genes cluster together in different experiments to recommend new genes in a pathway. Clusters are first identified within a single experiment series. Informative clusters, in which the user-supplied query genes appear together, are identified. New genes that cluster with the known genes, in a significant fraction of the informative clusters, are recommended. We implemented a prototype of our system and measured its performance on hundreds of pathways. We find that our method performs as well as an established approach while significantly increasing the speed and scalability of searching large datasets. [Supplemental material is available online at sysbio.soe.ucsc.edu/cluegene/psb07.]
authors.affiliation: Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Ng
authors.forename: Marcos H
authors.initials: MH
authors.lastname: Woehrmann
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Stuart
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D023281:Genomics / Q000706:statistics & numerical data*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated; D012984:Software