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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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Visualisation of GBM modules with B-score less than  and their inter-module connectivity.Nodes represent extracted modules, node size represents module size and node color represents (log-transformed) fold-change in average module gene expression level compared with normal patient samples (Red - increase in average expression, green - decrease in average expression, lavender - no change in average expression). Edge widths are proportional to connectivity (i.e., number of co-expression gene pairs) between module pairs.
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pone-0086693-g005: Visualisation of GBM modules with B-score less than and their inter-module connectivity.Nodes represent extracted modules, node size represents module size and node color represents (log-transformed) fold-change in average module gene expression level compared with normal patient samples (Red - increase in average expression, green - decrease in average expression, lavender - no change in average expression). Edge widths are proportional to connectivity (i.e., number of co-expression gene pairs) between module pairs.

Mentions: We applied the DiME algorithm with a B-score cutoff of to the two Rembrandt glioma datasets, and visualised the resulting modules and their interconnectivity in Figures 4 and 5. Each module is annotated with a specific function summarised from its enriched Gene Ontology terms (false discovery rate in hypergeometric tests). Edge widths are designed to be proportional to the number of connections (co-expression pairs) between two modules, in order to illustrate strength of coordination between functional components in the disease network. Node color represents fold change of average expression level of all genes in one module compared with normal patient samples.


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)

Visualisation of GBM modules with B-score less than  and their inter-module connectivity.Nodes represent extracted modules, node size represents module size and node color represents (log-transformed) fold-change in average module gene expression level compared with normal patient samples (Red - increase in average expression, green - decrease in average expression, lavender - no change in average expression). Edge widths are proportional to connectivity (i.e., number of co-expression gene pairs) between module pairs.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0086693-g005: Visualisation of GBM modules with B-score less than and their inter-module connectivity.Nodes represent extracted modules, node size represents module size and node color represents (log-transformed) fold-change in average module gene expression level compared with normal patient samples (Red - increase in average expression, green - decrease in average expression, lavender - no change in average expression). Edge widths are proportional to connectivity (i.e., number of co-expression gene pairs) between module pairs.
Mentions: We applied the DiME algorithm with a B-score cutoff of to the two Rembrandt glioma datasets, and visualised the resulting modules and their interconnectivity in Figures 4 and 5. Each module is annotated with a specific function summarised from its enriched Gene Ontology terms (false discovery rate in hypergeometric tests). Edge widths are designed to be proportional to the number of connections (co-expression pairs) between two modules, in order to illustrate strength of coordination between functional components in the disease network. Node color represents fold change of average expression level of all genes in one module compared with normal patient samples.

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