Browsing by Subject "Module detection"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery
    (BMC, 2021-10-25) Liu, Yusong; Ye, Xiufen; Yu, Christina Y.; Shao, Wei; Hou, Jie; Feng, Weixing; Zhang, Jie; Huang, Kun; Biostatistics & Health Data Science, School of Medicine
    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.