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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

Connection of network 2 with minor networks.Networks are built as previously described in Figure 3. Genes that are 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 direct interaction between gene products whereas dashed lines represents indirect interaction. Orange lines display interconnections between minor networks (N 20 and N 21) and major network 2.
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pone-0013518-g004: Connection of network 2 with minor networks.Networks are built as previously described in Figure 3. Genes that are 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 direct interaction between gene products whereas dashed lines represents indirect interaction. Orange lines display interconnections between minor networks (N 20 and N 21) and major network 2.

Mentions: At this level of analysis, the minor networks, which were at first considered to be less pertinent, should be re-analysed. For instance, network 21 structured around DPEP2 (dipeptidase 2) interacts through leukotriene D4 with up to three gene products belonging to 9 of the main networks. Similarly for network 20, CRTPA interacts through PSCD gene products with five main networks (data not shown). The interconnection of network 20 and 21 with the main network 2 is given in Figure 4. Overall, among the 202 eligible gene products from a knowledge database, 193 gene products were highly interconnected through 63 common gene products. The above genes structure the previously mentioned Meta network (Figure 2).


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)

Connection of network 2 with minor networks.Networks are built as previously described in Figure 3. Genes that are 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 direct interaction between gene products whereas dashed lines represents indirect interaction. Orange lines display interconnections between minor networks (N 20 and N 21) and major network 2.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0013518-g004: Connection of network 2 with minor networks.Networks are built as previously described in Figure 3. Genes that are 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 direct interaction between gene products whereas dashed lines represents indirect interaction. Orange lines display interconnections between minor networks (N 20 and N 21) and major network 2.
Mentions: At this level of analysis, the minor networks, which were at first considered to be less pertinent, should be re-analysed. For instance, network 21 structured around DPEP2 (dipeptidase 2) interacts through leukotriene D4 with up to three gene products belonging to 9 of the main networks. Similarly for network 20, CRTPA interacts through PSCD gene products with five main networks (data not shown). The interconnection of network 20 and 21 with the main network 2 is given in Figure 4. Overall, among the 202 eligible gene products from a knowledge database, 193 gene products were highly interconnected through 63 common gene products. The above genes structure the previously mentioned Meta network (Figure 2).

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