title: Using multivariate methods to infer knowledge from genomic data.
abstract: Since the introduction of genome sequencing techniques several methods for genomic data preprocessing and analysis have been published and applied to answer different biological questions. Rarely, multivariate methods have been used to extract knowledge about protein roles. Two of the most informative types of data are gene expression data (microarrays) and phylogenetic profiles indicating presence of genes in other organisms and therefore providing information about their co-evolution. Here we show that these two types of data, analyzed by means of principal component analysis and non parametric discriminant analysis, provide useful information about protein function and their participation in virulence processes.
authors: Statistics Department, Universidad Nacional de Colombia, Colombia. llopezk@unal.edu.co
authors: Liliana
authors: L
authors: López-Kleine
authors: Nicolás
authors: N
authors: Molano
authors: Luis
authors: L
authors: Ospina
mesh_terms: D030541:Databases, Genetic; D016002:Discriminant Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling; D016680:Genome, Bacterial; D051188:Knowledge Bases; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis; D018709:Statistics, Nonparametric; D013297:Streptococcus pyogenes / Q000235:genetics*; D037521:Virulence Factors / Q000235:genetics
chemical_list: D037521:Virulence Factors