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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
PathAct: a novel method for pathway analysis using gene expression profiles
Kaoru Mogushi1, Hiroshi Tanaka
1Department of Bioinformatics, Division of Medical Genomics, Medical Research Institute, Tokyo Medical and Dental University 24F M&D Tower Bldg., 1-5-45 Yushima, Bunkyo-ku, Tokyo, Japan.
Bioinformation
|June 11, 2013
Summary
PathAct is a new pathway analysis method that quantifies individual patient pathway activity. It shows good agreement with existing methods and enables further statistical analysis for gene expression profiles.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Gene expression profiling is crucial for understanding disease mechanisms.
- Conventional pathway analysis methods have limitations in quantifying individual patient pathway activity.
Purpose of the Study:
- To introduce PathAct, a novel method for pathway analysis.
- To quantitatively estimate pathway activity levels for individual patients.
- To evaluate PathAct's performance against established gene-enrichment analysis methods.
Main Methods:
- PathAct generates a pathway-by-sample matrix for quantitative pathway activity estimation.
- Comparison with Gene Set Enrichment Analysis (GSEA) using a breast cancer dataset (Dataset #1).
- Comparison with Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis using a breast cancer dataset with disease-free survival (Dataset #2).
Main Results:
- PathAct demonstrated good agreement with GSEA and DAVID analysis.
- Four out of six significant pathways identified by GSEA were also found by PathAct in Dataset #1.
- Two pathways identified by PathAct were also identified by DAVID in Dataset #2.
Conclusions:
- PathAct is a feasible and reliable method for pathway analysis.
- The pathway-by-sample matrix generated by PathAct facilitates further statistical analyses, including clustering and survival analysis.
- PathAct enhances the investigation of biological and clinical implications of gene expression profiles.
