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Updated: May 14, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

Scott M Gibson1, Stephen P Ficklin, Sven Isaacson

  • 1Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA.

Plos One
|February 15, 2013
PubMed
Summary
This summary is machine-generated.

Gene co-expression networks reveal biological functions. Enhanced Random Matrix Theory (RMT) improves network construction speed and shows high functional robustness across species, even with data variations.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Understanding gene relationships is crucial for systems biology.
  • Gene co-expression networks derived from microarray data help identify these relationships.
  • Random Matrix Theory (RMT) is a knowledge-independent method for thresholding networks.

Purpose of the Study:

  • To assess the functional robustness of gene co-expression networks under varying input data.
  • To develop a scalable tool for rapid network construction for robustness testing.
  • To investigate the functional robustness of networks in human, rice, and yeast.

Main Methods:

  • Enhanced an existing Random Matrix Theory (RMT) implementation for improved scalability.
  • Constructed hundreds of gene co-expression networks using perturbed input sample sets.
  • Tested network functional robustness in Homo sapiens, Oryza sativa, and Saccharomyces cerevisiae.

Main Results:

  • Achieved significant reductions in network construction time and computational resources.
  • Demonstrated high functional similarity between networks despite variations in global properties.
  • Confirmed that biological functions captured by RMT-thresholded co-expression networks are highly robust.

Conclusions:

  • The enhanced RMT implementation enables efficient and scalable construction of gene co-expression networks.
  • Gene co-expression networks, when thresholded by RMT, exhibit high functional robustness.
  • This robustness suggests reliable insights into biological functions across different datasets and species.