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multiWGCNA: an R package for deep mining gene co-expression networks in multi-trait expression data.

Dario Tommasini1, Brent L Fogel2,3,4

  • 1Department of Neurology, UCLA David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Gonda Room 6554A, Los Angeles, CA, 90095, USA.

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|March 25, 2023
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Summary

This study introduces multiWGCNA, a novel R package for analyzing gene co-expression networks across spatial or temporal conditions. It enables the study of disease-associated molecular pathways in dynamic biological contexts.

Keywords:
Astrocyte reactivityCo-expression networkDifferential co-expressionEAENeurological diseasePreservationTau pathologyWGCNArTg4510

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene co-expression networks reveal biological pathways and are crucial for studying disease transcriptional signatures.
  • Weighted Gene Co-expression Network Analysis (WGCNA) identifies trait-associated networks but typically requires both disease and wildtype samples.
  • Analyzing co-expression networks within specific spatiotemporal disease contexts is challenging due to a lack of comprehensive software.

Purpose of the Study:

  • To develop and introduce multiWGCNA, a WGCNA-based procedure for analyzing gene expression data with variable spatial or temporal traits.
  • To enable the study of disease-associated co-expression modules across different conditions and time points.
  • To provide a comprehensive software implementation for spatiotemporal co-expression network analysis in disease.

Main Methods:

  • Developed multiWGCNA, an R package extending WGCNA for datasets with spatial or temporal dimensions.
  • Constructed combined and condition-specific networks, mapping modules across designs.
  • Performed downstream analyses including module-trait correlation and module preservation.

Main Results:

  • multiWGCNA successfully identified the de novo formation of a neurotoxic astrocyte transcriptional program in experimental autoimmune encephalitis mouse models.
  • Demonstrated the utility of multiWGCNA in tracking the temporal evolution of pathological modules during disease progression in a tau pathology mouse model.
  • Showcased the application of multiWGCNA to RNA-sequencing data with spatial and temporal variables.

Conclusions:

  • The multiWGCNA R package is a valuable tool for analyzing two-dimensional expression data, particularly for disease-associated modules across time or space.
  • multiWGCNA facilitates a deeper understanding of molecular pathway dynamics in disease.
  • The software is freely available, promoting its adoption in the research community.