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DiME: a scalable disease module identification algorithm with application to glioma progression.

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

Bottom Line: We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules.We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.

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Comparison of module reproducibility among different algorithms.Shown are box plots of average reproducibility (Jaccard index) for each technique used. Asterisks denote statistical significance in Student's -tests when comparing means with MCODE modules: “*” - .
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pone-0086693-g006: Comparison of module reproducibility among different algorithms.Shown are box plots of average reproducibility (Jaccard index) for each technique used. Asterisks denote statistical significance in Student's -tests when comparing means with MCODE modules: “*” - .

Mentions: The results are shown as box plots of Jaccard index distributions in Figure 6. Average Jaccard indices of 0.28 and 0.51 were observed for the grade II and GBM datasets respectively, showing a high level of module reproducibility for both tumour grades considering the remarkable differences in the microarrays. Inspection of Gene Ontology enrichment of modules in the independent datasets also showed that they are functionally similar to the matched modules in the Rembrandt counterpart (data not shown). It may be seen from the GBM data box plots that under stringent B-score cutoffs () the upper quantiles of the Jaccard index distribution show markedly increased average values and decreased range of variation, compared with those of MCODE modules. The average Jaccard index for all DiME modules with is also significantly higher than that of the MCODE modules (Student's -test, ) in the GBM datasets, and a similar trend, though not highly significant (Student's -test, ), was observable for the grade II glioma datasets. Statistical insignificance may be attributed to the fact that the MCODE modules showed large variance in the Jaccard indices. It is noteworthy that the low-grade glioma data generally displayed considerably less reproducibility than that of the high-grade counterpart. This might be due to the relatively smaller sample size and possible heterogeneity in the samples (which might indicate existence of molecular subtypes across the patient cohorts).


DiME: a scalable disease module identification algorithm with application to glioma progression.

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

Comparison of module reproducibility among different algorithms.Shown are box plots of average reproducibility (Jaccard index) for each technique used. Asterisks denote statistical significance in Student's -tests when comparing means with MCODE modules: “*” - .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921127&req=5

pone-0086693-g006: Comparison of module reproducibility among different algorithms.Shown are box plots of average reproducibility (Jaccard index) for each technique used. Asterisks denote statistical significance in Student's -tests when comparing means with MCODE modules: “*” - .
Mentions: The results are shown as box plots of Jaccard index distributions in Figure 6. Average Jaccard indices of 0.28 and 0.51 were observed for the grade II and GBM datasets respectively, showing a high level of module reproducibility for both tumour grades considering the remarkable differences in the microarrays. Inspection of Gene Ontology enrichment of modules in the independent datasets also showed that they are functionally similar to the matched modules in the Rembrandt counterpart (data not shown). It may be seen from the GBM data box plots that under stringent B-score cutoffs () the upper quantiles of the Jaccard index distribution show markedly increased average values and decreased range of variation, compared with those of MCODE modules. The average Jaccard index for all DiME modules with is also significantly higher than that of the MCODE modules (Student's -test, ) in the GBM datasets, and a similar trend, though not highly significant (Student's -test, ), was observable for the grade II glioma datasets. Statistical insignificance may be attributed to the fact that the MCODE modules showed large variance in the Jaccard indices. It is noteworthy that the low-grade glioma data generally displayed considerably less reproducibility than that of the high-grade counterpart. This might be due to the relatively smaller sample size and possible heterogeneity in the samples (which might indicate existence of molecular subtypes across the patient cohorts).

Bottom Line: We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules.We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.

Show MeSH
Related in: MedlinePlus