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Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray.

Chuchana P, Holzmuller P, Vezilier F, Berthier D, Chantal I, Severac D, Lemesre JL, Cuny G, Nirdé P, Bucheton B - PLoS ONE (2010)

Bottom Line: Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes.We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation.We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Unité 844 - Montpellier, France. paul.chuchana@inserm.fr

ABSTRACT

Background: Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed.

Methodology/principal findings: To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes.

Conclusions/significance: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

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Related in: MedlinePlus

Close up of network.A maximum authorized number of 35 genes were used to generate a network. Direct interactions between each gene within a network were represented. Genes highlighted in green were down-regulated whereas genes in red were up-regulated. The number beside a gene name indicates its fold change expression. Genes in white, which were not found in the assay, were added by the data base as they are relevant to the network. Solid lines represent a direct interaction whereas a dashed line represents an indirect interaction.
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pone-0013518-g003: Close up of network.A maximum authorized number of 35 genes were used to generate a network. Direct interactions between each gene within a network were represented. Genes highlighted in green were down-regulated whereas genes in red were up-regulated. The number beside a gene name indicates its fold change expression. Genes in white, which were not found in the assay, were added by the data base as they are relevant to the network. Solid lines represent a direct interaction whereas a dashed line represents an indirect interaction.

Mentions: The other secondary networks are comprised of few gene products and cannot be directly associated to major networks, which signifies that only 9 out of the total of 202 gene products were not associated to major networks. In Figure 3, we present in more detail, the most likely network (i.e. network 1). It contains 10 under-expressed genes, which products are coloured green, and 13 over-expressed genes, which products are coloured red. The others molecules were not detected as variant in our experiment. This principal network is centred on NF-kappaB, which is known to play a central role in LPS activation. Figure 3 also shows that NF-kappaB interacts and upregulates target genes as illustrated by ICAM1.


Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray.

Chuchana P, Holzmuller P, Vezilier F, Berthier D, Chantal I, Severac D, Lemesre JL, Cuny G, Nirdé P, Bucheton B - PLoS ONE (2010)

Close up of network.A maximum authorized number of 35 genes were used to generate a network. Direct interactions between each gene within a network were represented. Genes highlighted in green were down-regulated whereas genes in red were up-regulated. The number beside a gene name indicates its fold change expression. Genes in white, which were not found in the assay, were added by the data base as they are relevant to the network. Solid lines represent a direct interaction whereas a dashed line represents an indirect interaction.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2958130&req=5

pone-0013518-g003: Close up of network.A maximum authorized number of 35 genes were used to generate a network. Direct interactions between each gene within a network were represented. Genes highlighted in green were down-regulated whereas genes in red were up-regulated. The number beside a gene name indicates its fold change expression. Genes in white, which were not found in the assay, were added by the data base as they are relevant to the network. Solid lines represent a direct interaction whereas a dashed line represents an indirect interaction.
Mentions: The other secondary networks are comprised of few gene products and cannot be directly associated to major networks, which signifies that only 9 out of the total of 202 gene products were not associated to major networks. In Figure 3, we present in more detail, the most likely network (i.e. network 1). It contains 10 under-expressed genes, which products are coloured green, and 13 over-expressed genes, which products are coloured red. The others molecules were not detected as variant in our experiment. This principal network is centred on NF-kappaB, which is known to play a central role in LPS activation. Figure 3 also shows that NF-kappaB interacts and upregulates target genes as illustrated by ICAM1.

Bottom Line: Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes.We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation.We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Unité 844 - Montpellier, France. paul.chuchana@inserm.fr

ABSTRACT

Background: Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed.

Methodology/principal findings: To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes.

Conclusions/significance: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

Show MeSH
Related in: MedlinePlus