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Updated: Jan 4, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Pathway-based meta-analysis for partially paired transcriptomics analysis.

Wing Tung Fung¹, Joseph T Wu¹, Wai Man Mandy Chan²

¹School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

Research Synthesis Methods

|November 5, 2019

Summary

This summary is machine-generated.

Researchers developed a new pathway-based meta-analysis method for paired or partially paired transcriptomics data. This approach enhances understanding of disease mechanisms, as demonstrated in psoriasis studies.

Keywords:

Meta-analysis Partially overlapping samples Pathway analysis Psoriasis

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Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

Pathway-based analysis integrates biological knowledge into transcriptomics.
Pathway-based meta-analysis combines results from multiple studies.
Existing methods lack support for paired or partially paired sample designs common in biomedical research.

Purpose of the Study:

To develop a novel pathway-based meta-analysis approach for paired or partially paired transcriptomics data.
To address the limitations of current methods in handling common biomedical study designs.

Main Methods:

Developed a new pathway-based meta-analysis framework.
Applied p-value-based, rank-based, and effect size-based algorithms.
Utilized partially paired transcriptomics data from psoriasis studies for demonstration.

Main Results:

Identified perturbed cell cycle and DNA replication pathways in psoriatic lesional skin.
Validated findings using independent RNA-Seq data.
Demonstrated higher detection power compared to existing pathway meta-analysis methods.

Conclusions:

The developed method effectively incorporates paired or partially paired samples in pathway-based meta-analysis.
The framework is applicable to various omics data, including transcriptomics.
Enhances understanding of disease mechanisms by leveraging biological domain knowledge.