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Network-based analysis of omics data: the LEAN method.

Frederik Gwinner1,2, Gwénola Boulday1,2, Claire Vandiedonck3,4

  • 1Univ Paris Diderot, Sorbonne Paris Cité, UMRS 1161, F-75010 Paris, France.

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Summary

Local Enrichment Analysis (LEAN) identifies statistically dysregulated subnetworks in omics data efficiently and without parameters. This method accurately detects biological signals, implicating specific genes for further research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Omics data analysis often involves long run times and heuristic solutions.
  • Existing methods for network analysis can be parameter-dependent and provide incomplete results.

Purpose of the Study:

  • Introduce Local Enrichment Analysis (LEAN) for identifying dysregulated subnetworks in genome-wide omics datasets.
  • Develop a parameter-free, efficient, and exhaustive method for subnetwork identification.

Main Methods:

  • LEAN utilizes a local subnetwork model, simplifying the common subnetwork approach.
  • The method allows for exact identification of statistically dysregulated local subnetworks.
  • LEAN directly implicates single genes for experimental follow-up.

Main Results:

  • LEAN demonstrated superior performance in detecting dysregulated subnetworks compared to standard approaches.
  • The method effectively reflects biological similarity across different experiments.
  • LEAN identified a significant local subnetwork around Von Willebrand Factor (VWF) in transcriptome data for Cerebral Cavernous Malformations (CCM), correlating with protein-level effects.

Conclusions:

  • LEAN provides an exact, parameter-free, and efficient method for identifying dysregulated subnetworks from omics data.
  • The R-package LEANR is available for implementing LEAN.
  • LEAN is broadly applicable for pinpointing statistically significant local subnetworks in various genome-scale datasets.