Functional clusters analysis and research based on differential coexpression networks
Differential coexpression analysis has gradually become an important approach to improve the conventional method of analysing differentially expressed genes. With this approach, it is possible to discover disease mechanisms and underlying regulatory dynamics which remain obscure in differential expr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Biotechnology & Biotechnological Equipment |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13102818.2017.1358669 |
| _version_ | 1819170840150802432 |
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| author | Shuai Meng Guixia Liu Lingtao Su Liyan Sun Di Wu Lingwei Wang Zhao Zheng |
| author_facet | Shuai Meng Guixia Liu Lingtao Su Liyan Sun Di Wu Lingwei Wang Zhao Zheng |
| author_sort | Shuai Meng |
| collection | DOAJ |
| description | Differential coexpression analysis has gradually become an important approach to improve the conventional method of analysing differentially expressed genes. With this approach, it is possible to discover disease mechanisms and underlying regulatory dynamics which remain obscure in differential expression analysis. The detection of differential coexpression links and functional clusters between different disease states is a demanding task. Nevertheless, there is no gold standard for detecting differential coexpression links and functional clusters. Consequently, we developed a novel fusion algorithm FDvDe (Fusion of differential vertex and differential edge) to detect differential coexpression links by aggregating the set of ‘differential vertex’ and ‘differential edge.’ Then, we constructed differential coexpression networks between normal and tumour states by integrating the differential coexpression links. With this approach, we identified 1823 genes and 29370 links. Then, we developed the algorithms GTHC (GO term hierarchical clusters) to identify functional modules. The distance matrix used in the hierarchical process was formed by the GO semantic similarity. Furthermore, we aggregated the densities among clusters describing the connectivity among clusters and topological property analysis to discover the hub genes and hub pathways which play an important role in disease mechanism. In this paper, we showed that our approach worked well on a data set of breast cancer samples (68 tumour samples) and normal samples (73 normal samples), and revealed the crucial role and biological significance of the modules and hub genes found in this approach. |
| first_indexed | 2024-12-22T19:41:47Z |
| format | Article |
| id | doaj.art-386e534dc92f4b758a69f2762987efd2 |
| institution | Directory Open Access Journal |
| issn | 1310-2818 1314-3530 |
| language | English |
| last_indexed | 2024-12-22T19:41:47Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Biotechnology & Biotechnological Equipment |
| spelling | doaj.art-386e534dc92f4b758a69f2762987efd22022-12-21T18:14:50ZengTaylor & Francis GroupBiotechnology & Biotechnological Equipment1310-28181314-35302018-01-0132117118210.1080/13102818.2017.13586691358669Functional clusters analysis and research based on differential coexpression networksShuai Meng0Guixia Liu1Lingtao Su2Liyan Sun3Di Wu4Lingwei Wang5Zhao Zheng6College of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityDifferential coexpression analysis has gradually become an important approach to improve the conventional method of analysing differentially expressed genes. With this approach, it is possible to discover disease mechanisms and underlying regulatory dynamics which remain obscure in differential expression analysis. The detection of differential coexpression links and functional clusters between different disease states is a demanding task. Nevertheless, there is no gold standard for detecting differential coexpression links and functional clusters. Consequently, we developed a novel fusion algorithm FDvDe (Fusion of differential vertex and differential edge) to detect differential coexpression links by aggregating the set of ‘differential vertex’ and ‘differential edge.’ Then, we constructed differential coexpression networks between normal and tumour states by integrating the differential coexpression links. With this approach, we identified 1823 genes and 29370 links. Then, we developed the algorithms GTHC (GO term hierarchical clusters) to identify functional modules. The distance matrix used in the hierarchical process was formed by the GO semantic similarity. Furthermore, we aggregated the densities among clusters describing the connectivity among clusters and topological property analysis to discover the hub genes and hub pathways which play an important role in disease mechanism. In this paper, we showed that our approach worked well on a data set of breast cancer samples (68 tumour samples) and normal samples (73 normal samples), and revealed the crucial role and biological significance of the modules and hub genes found in this approach.http://dx.doi.org/10.1080/13102818.2017.1358669Differential coexpression networkfusion algorithmsGOhierarchical clusterstopological property analysishub gene |
| spellingShingle | Shuai Meng Guixia Liu Lingtao Su Liyan Sun Di Wu Lingwei Wang Zhao Zheng Functional clusters analysis and research based on differential coexpression networks Biotechnology & Biotechnological Equipment Differential coexpression network fusion algorithms GO hierarchical clusters topological property analysis hub gene |
| title | Functional clusters analysis and research based on differential coexpression networks |
| title_full | Functional clusters analysis and research based on differential coexpression networks |
| title_fullStr | Functional clusters analysis and research based on differential coexpression networks |
| title_full_unstemmed | Functional clusters analysis and research based on differential coexpression networks |
| title_short | Functional clusters analysis and research based on differential coexpression networks |
| title_sort | functional clusters analysis and research based on differential coexpression networks |
| topic | Differential coexpression network fusion algorithms GO hierarchical clusters topological property analysis hub gene |
| url | http://dx.doi.org/10.1080/13102818.2017.1358669 |
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