title: Using Semantic Similarities and csbl.go for Analyzing Microarray Data.
abstract: Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challenges for interpreting results, as primary analysis often yields hundreds of genes. Gene Ontology (GO), a controlled vocabulary for gene products, enables semantic analysis of such gene sets. GO can be used to define semantic similarity between genes, which enables semantic clustering to reduce the complexity of a result set. Here, we describe how to compute semantic similarities and perform GO-based gene clustering using csbl.go, an R package for GO semantic similarity. We demonstrate the approach with expression profiles from breast cancer.
authors.affiliation: University of Helsinki, Biomedicum Helsinki (B524a), 63, 00014, Helsinki, Finland. kristian.ovaska@iki.fi.
authors.forename: Kristian
authors.initials: K
authors.lastname: Ovaska
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D063990:Gene Ontology; D006801:Humans; D058977:Molecular Sequence Annotation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012660:Semantics; D012984:Software
keywords: Data analysis; Expression microarray; Gene ontology; Hierarchical clustering; Measure; Semantic similarity