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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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csdR, an R package for differential co-expression analysis.

Jakob P Pettersen1, Eivind Almaas2,3

  • 1Department of Biotechnology and Food Science, NTNU- Norwegian University of Science and Technology, Trondheim, Norway.

BMC Bioinformatics
|February 20, 2022
PubMed
Summary

The new R-package csdR improves differential co-expression network analysis by offering better performance and user-friendliness. This tool helps researchers analyze biological phenotypes and diseases more efficiently.

Keywords:
Co-expressionGene networkGenome-scaleNetworkR

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential co-expression network analysis is crucial for understanding biological phenotypes and diseases.
  • The CSD algorithm generates these networks by scoring gene pair co-expression across conditions.
  • Existing CSD implementations face challenges with performance, usability, and documentation.

Purpose of the Study:

  • To develop an improved R-package, csdR, for differential co-expression analysis.
  • To enhance computational performance, user-friendliness, and documentation compared to existing methods.
  • To ensure compatibility with other bioinformatics tools.

Main Methods:

  • Development of the csdR R-package.
  • Benchmarking csdR against existing CSD implementations using a realistic dataset of 20,645 genes.
  • Performance evaluation considering robustness and parallel processing capabilities.

Main Results:

  • csdR demonstrated superior performance compared to one existing CSD implementation; the other failed to run.
  • The package can utilize multiple processing cores, achieving a speedup of approximately 2.7x with around 10 cores.
  • Robustness was verified through an appropriate number of iterations.

Conclusions:

  • csdR is a valuable tool for differential co-expression analysis.
  • The package generates robust results efficiently, suitable for datasets of realistic sizes.
  • It can be effectively utilized on workstations or compute servers within a standard workday.