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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: 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.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.

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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Mean rank ratio of GroupRank using different distance thresholds.The gene groups in GroupRank are partitioned based on a distance threshold with a gradient from 0.1 to 0.9. From A to D, the cancer types are lung cancer, kidney cancer, leukemia and breast cancer.
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pone-0110406-g001: Mean rank ratio of GroupRank using different distance thresholds.The gene groups in GroupRank are partitioned based on a distance threshold with a gradient from 0.1 to 0.9. From A to D, the cancer types are lung cancer, kidney cancer, leukemia and breast cancer.

Mentions: We tested GroupRank in four cancer related microarray datasets (lung, kidney, leukemia, and breast cancer) individually. Mean rank ratio (MRR) of known disease genes predicted by our algorithm was used to evaluate its overall performance. By adjusting the threshold of distance d from 0 to 1 with the gradient of 0.01 in defining a gene group, the best MRR was obtained when an optimized threshold was chosen (Table 1). In the results, the best thresholds of distance for different cancer types fell into 0.2–0.6 (Figure 1). A possible explanation is that using a more rigorous threshold, there are not enough effective groups that can be formed, while all genes are possibly classified into very few super groups with poor correlations if a more relaxed threshold is applied.


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)

Mean rank ratio of GroupRank using different distance thresholds.The gene groups in GroupRank are partitioned based on a distance threshold with a gradient from 0.1 to 0.9. From A to D, the cancer types are lung cancer, kidney cancer, leukemia and breast cancer.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110406-g001: Mean rank ratio of GroupRank using different distance thresholds.The gene groups in GroupRank are partitioned based on a distance threshold with a gradient from 0.1 to 0.9. From A to D, the cancer types are lung cancer, kidney cancer, leukemia and breast cancer.
Mentions: We tested GroupRank in four cancer related microarray datasets (lung, kidney, leukemia, and breast cancer) individually. Mean rank ratio (MRR) of known disease genes predicted by our algorithm was used to evaluate its overall performance. By adjusting the threshold of distance d from 0 to 1 with the gradient of 0.01 in defining a gene group, the best MRR was obtained when an optimized threshold was chosen (Table 1). In the results, the best thresholds of distance for different cancer types fell into 0.2–0.6 (Figure 1). A possible explanation is that using a more rigorous threshold, there are not enough effective groups that can be formed, while all genes are possibly classified into very few super groups with poor correlations if a more relaxed threshold is applied.

Bottom Line: 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.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.

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