gsean

gsean implements network-informed Gene Set Enrichment Analysis (GSEA) in R/Bioconductor by constructing a gene co-expression network from the input expression matrix to refine enrichment calculations and provide a systems-level perspective on pathway and gene-set activity.


Key Features:

  • R/Bioconductor implementation: Provided as an R/Bioconductor package for integration with Bioconductor workflows and data structures.
  • Network construction: Constructs a biological network, typically a gene co-expression network, directly from the input expression matrix.
  • Network-informed enrichment: Incorporates derived network topology into GSEA to refine enrichment calculations and account for coordinated gene behavior.
  • Regulatory and co-expression capture: Captures regulatory structure, co-expression patterns, and functional modules that are lost in gene-independent analyses.
  • Systems-level interpretation: Provides a systems-level perspective on functional activity and pathway activation patterns.
  • Improved precision and interpretability: Enhances the precision and interpretability of enrichment results for processes driven by gene–gene interactions.
  • Interaction-driven pathway detection: Identifies pathways or gene sets whose activity emerges from structured interactions rather than marginal changes in single genes.

Scientific Applications:

  • Systems biology: Analyzing pathway activation and module activity in systems biology studies.
  • Regulatory network inference: Interpreting regulatory network signals by integrating co-expression topology into enrichment analysis.
  • High-dimensional transcriptomic analysis: Detecting pathway-level signals in high-dimensional transcriptomic datasets that are not evident at the single-gene level.
  • Pathway activity detection: Revealing pathway activation patterns that traditional list-based enrichment approaches may miss.

Methodology:

Derives network topology from the expression data by constructing a gene co-expression network from the input expression matrix and incorporates that topology into Gene Set Enrichment Analysis to refine enrichment calculations.

Topics

Collections

Details

License:
Artistic-2.0
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
7/19/2018
Last Updated:
12/10/2018

Operations

Data Inputs & Outputs

Gene-set enrichment analysis

Other operations do not define inputs or outputs.

Documentation

Downloads

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