Comput Math Methods Med - A Bayesian approach for decision making on the identification of genes with different expression levels: an application to Escherichia coli bacterium data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ data(2317) use(1299) case(1017) }
{ estim(2440) model(1874) function(577) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ concept(1167) ontolog(924) domain(897) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ model(2656) set(1616) predict(1553) }
{ sampl(1606) size(1419) use(1276) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ data(1714) softwar(1251) tool(1186) }
{ howev(809) still(633) remain(590) }
{ spatial(1525) area(1432) region(1030) }
{ can(981) present(881) function(850) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ clinic(1479) use(1117) guidelin(835) }
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{ signal(2180) analysi(812) frequenc(800) }
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{ cancer(2502) breast(956) screen(824) }
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Resumo

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

Resumo Limpo

common interest gene express data analysi identifi larg pool candid gene gene present signific chang express level treatment control biolog condit usual done use statist valu cutoff valu use separ gene differenti nondifferenti express paper propos bayesian approach identifi gene differenti express calcul sequenti credibl interv predict densiti construct use sampl mean treatment effect gene studi exclud treatment effect gene previous identifi statist evid differ compar bayesian approach standard one base use ttest modifi ttest via simul studi use small sampl size common gene express data analysi result obtain report evid propos approach perform better standard one especi case mean differ increas treatment varianc relat control varianc also appli methodolog wellknown public avail data set escherichia coli bacterium

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