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GroupRank: rank candidate genes in PPI network by differentially expressed gene groups.

Wang Q, Zhang S, Pang S, Zhang M, Wang B, Liu Q, Li J - PLoS ONE (2014)

Bottom Line: Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated.A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network.Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

ABSTRACT
Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-expressed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

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MRR Comparisons of GroupRank using co-expressed and random groups.The red sign represents MRR of GroupRank using co-expressed gene groups in four cancers. Boxplots show the distributions of MRRs using random groups of the same size. The random sampling was repeated 1000 times in each cancer type.
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pone-0110406-g004: MRR Comparisons of GroupRank using co-expressed and random groups.The red sign represents MRR of GroupRank using co-expressed gene groups in four cancers. Boxplots show the distributions of MRRs using random groups of the same size. The random sampling was repeated 1000 times in each cancer type.

Mentions: In the GroupRank algorithm, we assumed that the differentially co-expressed gene groups are surrounding a good disease gene and thus are effective to rank disease gene candidates. In order to validate this assumption, we compared the ranking performance using co-expressed and random gene groups. The random groups having the same size were generated by randomly sampling from the PPI network. We repeated the sampling 1000 times. The results indicate that, in all four cancers we studied, the mean rank ratios using co-expressed groups are significantly better than using random gene groups (p-value<0.05) (see Figure 4). It suggested that the downstream genes of a strong disease gene tend to be co-expressed into a number of groups.


GroupRank: rank candidate genes in PPI network by differentially expressed gene groups.

Wang Q, Zhang S, Pang S, Zhang M, Wang B, Liu Q, Li J - PLoS ONE (2014)

MRR Comparisons of GroupRank using co-expressed and random groups.The red sign represents MRR of GroupRank using co-expressed gene groups in four cancers. Boxplots show the distributions of MRRs using random groups of the same size. The random sampling was repeated 1000 times in each cancer type.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4199715&req=5

pone-0110406-g004: MRR Comparisons of GroupRank using co-expressed and random groups.The red sign represents MRR of GroupRank using co-expressed gene groups in four cancers. Boxplots show the distributions of MRRs using random groups of the same size. The random sampling was repeated 1000 times in each cancer type.
Mentions: In the GroupRank algorithm, we assumed that the differentially co-expressed gene groups are surrounding a good disease gene and thus are effective to rank disease gene candidates. In order to validate this assumption, we compared the ranking performance using co-expressed and random gene groups. The random groups having the same size were generated by randomly sampling from the PPI network. We repeated the sampling 1000 times. The results indicate that, in all four cancers we studied, the mean rank ratios using co-expressed groups are significantly better than using random gene groups (p-value<0.05) (see Figure 4). It suggested that the downstream genes of a strong disease gene tend to be co-expressed into a number of groups.

Bottom Line: Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated.A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network.Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

ABSTRACT
Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-expressed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

Show MeSH
Related in: MedlinePlus