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Distance correlation application to gene co-expression network analysis.

Jie Hou1,2, Xiufen Ye3, Weixing Feng1

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin, China.

BMC Bioinformatics
|February 23, 2022
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Summary
This summary is machine-generated.

Distance correlation offers a superior method for analyzing gene expression, revealing complex biological relationships beyond traditional metrics. This approach enhances gene co-expression network construction and analysis for more stable and meaningful biological insights.

Keywords:
Distance correlationEnrichment analysisGene expressionWGCNA

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene co-expression network construction requires robust correlation metrics.
  • Traditional linear (Pearson) and monotonic (Spearman) correlations inadequately capture complex biological system dynamics.
  • There is a need for more informative correlation metrics in gene co-expression analysis.

Purpose of the Study:

  • To evaluate distance correlation as an advanced metric for gene co-expression analysis.
  • To introduce and assess a novel method, Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA).

Main Methods:

  • Distance correlation was compared against Pearson's correlation, Spearman's correlation, and maximal information coefficient.
  • The metrics were tested on diverse datasets including array (macrophage, liver) and RNA-seq (cervical cancer, pancreatic cancer).
  • Distance correlation was integrated into Weighted Gene Co-expression Network Analysis (WGCNA) to create DC-WGCNA.

Main Results:

  • Distance correlation demonstrated superior performance in capturing complex relationships and handling outliers, being distribution-free.
  • DC-WGCNA outperformed traditional WGCNA by enhancing enrichment analysis results.
  • The novel DC-WGCNA method showed improved module stability compared to standard WGCNA.

Conclusions:

  • Distance correlation effectively reveals intricate biological relationships in gene profiles, leading to more meaningful gene co-expression modules.
  • The application of distance correlation in WGCNA provides a more powerful tool for biological network analysis.
  • Increased computational memory requirements due to distance correlation's time complexity present a limitation.