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Background: Mining gene modules from genomic data is an important step todetect gene members of pathways or other relations such as protein-proteininteractions. In this work, we explore the plausibility of detecting genemodules by factorizing gene-phenotype associations from a phenotype ontologyrather than the conventionally used gene expression data. In particular, thehierarchical structure of ontology has not been sufficiently utilized inclustering genes while functionally related genes are consistently associatedwith phenotypes on the same path in the phenotype ontology. Results: We proposea hierarchal Nonnegative Matrix Factorization (NMF)-based method, calledConsistent Multiple Nonnegative Matrix Factorization (CMNMF), to factorizegenome-phenome association matrix at two levels of the hierarchical structurein phenotype ontology for mining gene functional modules. CMNMF constrains thegene clusters from the association matrices at two consecutive levels to beconsistent since the genes are annotated with both the child phenotype and theparent phenotype in the consecutive levels. CMNMF also restricts the identifiedphenotype clusters to be densely connected in the phenotype ontology hierarchy.In the experiments on mining functionally related genes from mouse phenotypeontology and human phenotype ontology, CMNMF effectively improved clusteringperformance over the baseline methods. Gene ontology enrichment analysis wasalso conducted to reveal interesting gene modules. Conclusions: Utilizing theinformation in the hierarchical structure of phenotype ontology, CMNMF canidentify functional gene modules with more biological significance than theconventional methods. CMNMF could also be a better tool for predicting membersof gene pathways and protein-protein interactions. Availability:https://github.com/nkiip/CMNMF

Mining Functional Modules by Multiview-NMF of Phenome-Genome Association