@article {574,
	title = {Multidimensional gene set analysis of genomic data.},
	journal = {PLoS One},
	volume = {5},
	year = {2010},
	month = {2010 Apr 27},
	pages = {e10348},
	abstract = {<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>},
	keywords = {Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Genomics, Humans, Models, Statistical},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0010348},
	author = {Montaner, David and Dopazo, Joaquin}
}
@article {590,
	title = {Direct functional assessment of the composite phenotype through multivariate projection strategies.},
	journal = {Genomics},
	volume = {92},
	year = {2008},
	month = {2008 Dec},
	pages = {373-83},
	abstract = {<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>},
	keywords = {Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Humans, Mathematical Computing, Multivariate Analysis, Phenotype},
	issn = {1089-8646},
	doi = {10.1016/j.ygeno.2008.05.015},
	author = {Conesa, Ana and Bro, Rasmus and Garcia-Garcia, Francisco and Prats, Jos{\'e} Manuel and G{\"o}tz, Stefan and Kjeldahl, Karin and Montaner, David and Dopazo, Joaquin}
}
