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dc.contributor.authorMerlo, Domenico Franco
dc.contributor.authorGmuender, Hans
dc.contributor.authorvan Leeuwen, Danitsja
dc.contributor.authorPatrone, Fioravante
dc.contributor.authorKleinjans, Jos
dc.contributor.authorvan Delft, Joost
dc.contributor.authorBonassi, Stefano
dc.contributor.authorMoretti, Stefano
HAL ID: 739814
ORCID: 0000-0003-3627-3257
dc.subjectComparative Analysis of Shapley valueen
dc.subjectShapley valueen
dc.subjectgame theoryen
dc.subjectgene expression analysisen
dc.titleCombining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollutionen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherDIPTEM, University of Genova, Genova;Italie
dc.contributor.editoruniversityotherEpidemiology and Biostatistics, National Cancer Research Institute, Genova;Italie
dc.contributor.editoruniversityotherGenedata AG, Basel;Suisse
dc.contributor.editoruniversityotherUnit of Molecular Epidemiology, National Cancer Research Institute, Genova;Italie
dc.contributor.editoruniversityotherDepartment of Health Risk and Toxicology, Maastricht University, Maastricht;Pays-Bas
dc.description.abstractenBackground In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways.en
dc.relation.isversionofjnlnameBMC Bioinformatics
dc.subject.ddclabelProbabilités et mathématiques appliquéesen

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