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NetTDP: permutation-based true discovery proportions for differential co-expression network analysis.

Menglan Cai1, Anna Vesely2, Xu Chen3

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xianning West, 710049, Shaanxi, China.

Briefings in Bioinformatics
|October 9, 2022
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Summary

A new method, Network True Discovery Proportions (NetTDP), quantifies and localizes differences in gene co-expression networks between sample groups. This approach identifies specific differing edges and nodes, improving differential network analysis.

Keywords:
difference quantification and localizationdifferential co-expression networkstrue discovery proportion

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Existing differential network analysis methods lack the ability to quantify and pinpoint specific network differences between sample groups.
  • Accurate identification of differential co-expression networks is crucial for understanding biological system variations.

Purpose of the Study:

  • To introduce a novel method, permutation-based Network True Discovery Proportions (NetTDP), for quantifying and localizing differences in co-expression networks.
  • To enable the identification of specific differing edges (correlations) and nodes (genes) between biological sample groups.

Main Methods:

  • Development of an edge-level and a node-level statistic within the NetTDP framework.
  • Utilizing a permutation-based sumSome method for detecting true discoveries of differential co-expression.
  • Enabling post hoc inference on user-defined or data-driven subsets of edges or genes.

Main Results:

  • The NetTDP method effectively quantifies the number of differing edges and nodes in co-expression networks.
  • The method successfully localizes these differences within specific network subsets.
  • Validation through simulation studies and analysis of five real biological datasets demonstrates the method's effectiveness.

Conclusions:

  • NetTDP provides a robust solution for quantifying and localizing differential co-expression networks.
  • The method's ability to analyze data-driven modules or biology-driven gene sets enhances its biological interpretability.
  • NetTDP offers a significant advancement in differential network analysis, applicable even when sub-networks are optimized using the same data.