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

Saraiva EF, Louzada F, Milan LA, Meira S, Cobre J - Comput Math Methods Med (2012)

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: FACET, Universidade Federal da Grande Dourados, Brazil.

ABSTRACT
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.

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Related in: MedlinePlus

Treatment and control observed means and variances and genes identified with evidence for difference by PA.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig2: Treatment and control observed means and variances and genes identified with evidence for difference by PA.

Mentions: Results for PA are presented in Figure 2. Results for TT, CT, and BTT are presented in Figure 3. These figures show the observed treatment and control means and variances of genes identified with evidence for difference by considering PA, TT, CT, and BTT, respectively. The PA identifies 340 genes with evidences for difference, while TT identifies 222, CT 219, and BTT 288 genes.


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

Saraiva EF, Louzada F, Milan LA, Meira S, Cobre J - Comput Math Methods Med (2012)

Treatment and control observed means and variances and genes identified with evidence for difference by PA.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3306789&req=5

fig2: Treatment and control observed means and variances and genes identified with evidence for difference by PA.
Mentions: Results for PA are presented in Figure 2. Results for TT, CT, and BTT are presented in Figure 3. These figures show the observed treatment and control means and variances of genes identified with evidence for difference by considering PA, TT, CT, and BTT, respectively. The PA identifies 340 genes with evidences for difference, while TT identifies 222, CT 219, and BTT 288 genes.

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: FACET, Universidade Federal da Grande Dourados, Brazil.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus