02112nas a2200241   4500008004100000022001400041245005600055210005500111260001600166300001100182490000600193520139200199653002301591653003001614653002901644653001801673653001301691653001101704653002401715100002001739700002001759856009101779       2010                        eng d  a1932-620300aMultidimensional gene set analysis of genomic data.0 aMultidimensional gene set analysis of genomic data  c2010 Apr 27  ae103480 v53 a<p>Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.</p>10aDatabases, Genetic10aGene Expression Profiling10aGene Regulatory Networks10aGenome, Human10aGenomics10aHumans10aModels, Statistical1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/multidimensional-gene-set-analysis-genomic-data01951nas a2200337   4500008004100000022001400041245010400055210006900159260001300228300001100241490000700252520087500259653002101134653002601155653002301181653001101204653003001215653001101245653002701256653002601283653001401309100001601323700001601339700002901355700002501384700001801409700002001427700002001447700002001467856012601487       2008                        eng d  a1089-864600aDirect functional assessment of the composite phenotype through multivariate projection strategies.0 aDirect functional assessment of the composite phenotype through   c2008 Dec  a373-830 v923 a<p>We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.</p>10aBreast Neoplasms10aComputational Biology10aDatabases, Genetic10aFemale10aGene Expression Profiling10aHumans10aMathematical Computing10aMultivariate Analysis10aPhenotype1 aConesa, Ana1 aBro, Rasmus1 aGarcia-Garcia, Francisco1 aPrats, José, Manuel1 aGötz, Stefan1 aKjeldahl, Karin1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/direct-functional-assessment-composite-phenotype-through-multivariate-projection-0