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Gene Coexpression Network Comparison via Persistent Homology.

Ali Nabi Duman1, Harun Pirim2

  • 1Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

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|October 18, 2018
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Summary
This summary is machine-generated.

Persistent homology, a topological data analysis method, successfully distinguished stress factors in Arabidopsis using weighted gene coexpression networks. This approach reveals similar immunoresponses share topological features.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Topological Data Analysis (TDA) offers novel methods for biological data analysis.
  • Persistent homology (PH) is a key TDA technique for characterizing shape and structure.
  • Weighted Gene Coexpression Networks (WGCNs) are valuable for understanding gene relationships.

Purpose of the Study:

  • To apply persistent homology to compare WGCNs derived from microarray data.
  • To evaluate the efficacy of PH in distinguishing plant immunoresponses to various stress factors.
  • To introduce a novel application of PH for network comparison in systems biology.

Main Methods:

  • Construction of WGCNs from 38 Arabidopsis thaliana microarray datasets.
  • Calculation of persistent homology to quantify multiscale topological features of each WGCN.
  • Application of hierarchical clustering using bottleneck distances between network topological summaries.

Main Results:

  • Persistent homology successfully differentiated WGCNs based on distinct stress factors.
  • Networks associated with similar immunoresponses exhibited close topological features in the bottleneck distance metric.
  • The method demonstrated computational efficiency in analyzing complex biological networks.

Conclusions:

  • Persistent homology is a powerful and efficient tool for analyzing WGCNs and distinguishing biological states.
  • Topological features of WGCNs correlate with specific immunoresponses, offering insights into stress factor effects.
  • This approach provides a foundation for advanced network analysis in plant biology and other fields.