title: Asymmetric latent semantic indexing for gene expression experiments visualization.
abstract: We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture model applied in the genes latent space. We describe the steps of the procedure and we show its utility in the Human Cancer dataset.
authors.affiliation: * Department of Computer Science, Sheffield Institute for Translational Neuroscience, University of Sheffield, Glossop Road S10 2HQ, Sheffield, UK.
authors.forename: Javier
authors.initials: J
authors.lastname: González
authors.affiliation: † Department of Statistics, University Carlos III of Madrid, Spain. C/Madrid, 126-28903, Getafe (Madrid), Spain.
authors.forename: Alberto
authors.initials: A
authors.lastname: Muñoz
authors.affiliation: † Department of Statistics, University Carlos III of Madrid, Spain. C/Madrid, 126-28903, Getafe (Madrid), Spain.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Martos
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015870:Gene Expression; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D012660:Semantics
keywords: Latent semantic indexing; asymmetric similarity; gene expression; kernel