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Updated: Aug 9, 2026
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Calculating the statistical significance of changes in pathway activity from gene expression data
Jörg Rahnenführer1, Francisco S Domingues, Jochen Maydt
1Max-Planck-Institute for Informatics, Saarbrücken, Germany. rahnenfj@mpi-sb.mpg.de
Summary
We developed a statistical method to score metabolic pathway activity changes using gene expression data. This approach enhances biological relevance and interpretability by considering gene co-regulation and pathway topology.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Gene expression data analysis often struggles to capture complex biological pathway activities.
- Existing methods for gene set enrichment analysis have limitations in sensitivity and interpretability.
- Inferring genetic networks solely from gene expression data can only reveal local structures.
Purpose of the Study:
- To present a novel statistical approach for scoring metabolic pathway activity changes from gene expression data.
- To improve the sensitivity and interpretability of findings from microarray experiments by integrating prior biological network knowledge.
- To develop methods that consider pairwise gene co-regulation and pathway topology for more accurate pathway activity assessment.
Main Methods:
- A hypothesis-driven statistical approach using predefined biological networks.
- Development of scoring metrics that incorporate all gene set members and account for pairwise co-regulation.
- Utilizing a nonparametric permutation test to calculate the significance of gene set co-regulation.
- Algorithms for optimal gene-to-enzyme mapping and integration of pathway topology (enzyme distances) to enhance sensitivity.
Main Results:
- The proposed method effectively identifies biologically relevant pathways with statistical significance.
- Adaptive measures for gene co-regulation within pathways were identified.
- Improved sensitivity in detecting relevant pathways through the integration of pathway topology.
- Functional assignment of genes to pathways was demonstrated in selected cases.
Conclusions:
- The statistical approach significantly enhances the analysis of metabolic pathway activity from gene expression data.
- Integrating gene co-regulation and pathway topology provides a more sensitive and interpretable method for biological discovery.
- The developed algorithms offer solutions for gene-enzyme mapping ambiguity and improve pathway detection accuracy.
