Distance correlation application to gene co-expression network analysis

BMC Bioinformatics

Xiufen Ye¹  Yusong Liu¹  Weixing Feng¹  Yatong Han¹  Jie Hou²  Yufen Wei³  Yu Li⁴  Qiaosheng Zhang⁵ 

[1] College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin, China;College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin, China;College of Science, Heilongjiang Bayi Agricultural University, Xinfeng Road, Daqing, China;College of Science, Heilongjiang Bayi Agricultural University, Xinfeng Road, Daqing, China;College of Science, Northeast Forestry University, Hexing Road, Harbin, China;School of Computer Engineering, Jiangsu Ocean University, Cangwu Road, Lianyungang, China;

关键词: Gene expression;    Distance correlation;    WGCNA;    Enrichment analysis;   

DOI  :  10.1186/s12859-022-04609-x

来源: Springer

【 摘 要 】

BackgroundTo construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson’s correlation) and monotonic (such as Spearman’s correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic.ResultsIn this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson’s correlation, Spearman’s correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability.ConclusionsDistance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.

【 授权许可】

CC BY