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Perform Pathway Enrichment Analysis Using ReactomeFIViz.

Robin Haw1, Fred Loney2, Edison Ong3

  • 1Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, ON, Canada.

Methods in Molecular Biology (Clifton, N.J.)
|October 5, 2019
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Summary
This summary is machine-generated.

This chapter details using ReactomeFIViz for pathway enrichment and Gene Set Enrichment Analysis (GSEA) on gene scores. It enables functional insights into large-scale biological data analysis and pathway visualization.

Keywords:
Biological pathwayCytoscapeGSEAGene scorePathway enrichment analysisReactomeReactomeFIViz

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Large-scale biological data analysis generates significant gene sets with associated scores.
  • Pathway-based approaches are crucial for understanding the functional context of these genes.
  • Reactome is a comprehensive pathway knowledge base supporting pathway-based analysis.

Purpose of the Study:

  • To describe procedures for pathway enrichment analysis using ReactomeFIViz.
  • To demonstrate two analysis types: pathway enrichment and Gene Set Enrichment Analysis (GSEA).
  • To illustrate overlaying gene scores on pathway diagrams for mechanistic insights.

Main Methods:

  • Utilizing ReactomeFIViz, a Cytoscape application built on Reactome pathways.
  • Performing pathway enrichment analysis on a set of significant genes.
  • Conducting GSEA using gene scores without a predefined cutoff.

Main Results:

  • Demonstration of ReactomeFIViz for pathway enrichment and GSEA.
  • Capability to overlay gene scores onto pathway diagrams.
  • Facilitation of understanding underlying mechanisms of pathway regulation.

Conclusions:

  • ReactomeFIViz provides a robust platform for pathway and network-based analysis.
  • The described methods enable comprehensive functional interpretation of gene expression data.
  • Visualizing gene scores on pathways enhances understanding of biological mechanisms.