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Updated: Aug 5, 2026
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for
Enock Niyonkuru1,2, Umair Khan1,2, Xinyu Tang1
1Bakar Computational Health Sciences Institute, University of California, San Francisco; San Francisco, CA, USA.
Biorxiv : the Preprint Server for Biology
|August 1, 2026
Summary
We developed CDRPipe, a computational drug repurposing pipeline, to integrate diverse transcriptomic data for accelerated therapeutic discovery. Integrating single-cell and microarray data significantly improves drug recovery and identifies novel candidates.
Area of Science:
- Pharmacology and Bioinformatics
- Genomics and Transcriptomics
- Computational Biology
Background:
- Transcriptome-based drug repurposing accelerates therapeutic discovery but faces challenges from fragmented data and inconsistent quality.
- Existing methods often rely on single perturbation databases, limiting comprehensive analysis.
Purpose of the Study:
- To develop CDRPipe (Computational Drug Repurposing Pipeline), a unified framework for integrating diverse transcriptomic perturbation data.
- To improve the robustness and interpretability of drug repurposing predictions by harmonizing data from different experimental technologies.
Main Methods:
- CDRPipe harmonizes microarray and single-cell RNA sequencing perturbation profiles.
- It standardizes preprocessing, computes rank-based connectivity scores, and uses empirical null models for significance evaluation.
- Applied to 233 disease signatures and evaluated using known drug-disease associations.
Main Results:
- Single-cell data recovered significantly more therapeutics than microarray data (50.0% vs. 6.2% recall).
- The two data resources were highly complementary, with only 3.5% overlap, expanding therapeutic coverage when integrated.
- Case studies demonstrated recovery of clinically relevant therapies and technology-dependent discovery patterns.
Conclusions:
- Integrating heterogeneous transcriptomic perturbation resources enhances drug repurposing.
- CDRPipe provides a robust and interpretable framework for identifying novel therapeutic candidates.
- This approach accelerates drug discovery by leveraging diverse, high-quality transcriptomic datasets.
