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
Outputs
Other operations do not define inputs or outputs.