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-data