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Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

ABSTRACT: BACKGROUND: 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.

SUBMITTER: Moretti S

PROVIDER: S-EPMC2556684 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

Moretti Stefano S van Leeuwen Danitsja D Gmuender Hans H Bonassi Stefano S van Delft Joost J Kleinjans Jos J Patrone Fioravante F Merlo Domenico Franco DF

BMC bioinformatics 20080902

<h4>Background</h4>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 repre ...[more]

PMID: 18764936

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Project description:Air pollutants including particulate matter (PM) and chemicals adsorbed onto PM pose a serious threat to human health. In this study, we analyzed the ability of PM to induce diverse gene expression profile modulation after chronic exposure in subjects living in two regions of the Czech Republic differing in levels and sources of the air pollution. We also considered impact of different seasonal conditions on concentrations and compositions of PM. Blood samples of 312 subjects from polluted Ostrava city and 154 controls from Prague city were collected in winter 2009, summer 2009 and winter 2010. The highest concentrations of air pollutants were detected in winter 2010 when the subjects were exposed to: PM of aerodynamic diameter < 2.5 M-BM-5m (70 vs. 44.9 M-BM-5g/m3); benzo[a]pyrene (9.02 vs. 2.56 ng/m3) and benzene (10.2 vs. 5.5 M-BM-5g/m3) in Ostrava and Prague, respectively. Global gene expression analysis of total RNA extracted from leukocytes was performed using whole genome microarrays (Illumina). The expression of selected genes was verified by quantitative real-time PCR (qRT-PCR). Despite lower concentrations of air pollutants we found a higher number of differentially expressed genes and affected KEGG pathways in subjects from Prague. In both locations we observed differences between seasons. The qRT-PCR analysis showed a significant decrease in expression of APEX, ATM, FAS, GSTM1, IL1B and RAD21 in subjects from Ostrava, in a comparison of winter 2010 and summer 2009. In Prague, an increase in gene expression was observed for GADD45A and PTGS2. In conclusion, high concentrations of pollutants in Ostrava do not increase the number of differentially expressed genes. This may be explained by adaption of humans to chronic exposure to air pollution. Total RNA was extracted from leukocytes of total of 154 control subjects and 312 subjects exposed to heavy air pollution. The samples were collected in three seasons (winter 2009, summer 2009, winter 2010) with different levels of air pollution. Most of the subjects were sampled repeatedly; however, some of them joined the study in summer 2009 or winter 2010.

Dataset Information

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

Publications

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.

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