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TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

Authors :
Yusong Liu
Xiufen Ye
Christina Y. Yu
Wei Shao
Jie Hou
Weixing Feng
Jie Zhang
Kun Huang
Source :
BMC Bioinformatics, Vol 22, Iss S4, Pp 1-19 (2021)
Publication Year :
2021
Publisher :
BMC, 2021.

Abstract

Abstract Background Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. Results In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. Conclusion In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Details

Language :
English
ISSN :
14712105
Volume :
22
Issue :
S4
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.3a92de7478ba4401933a0b36bf455721
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-021-03964-5