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

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Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc.

Xiqi Liao¹, Yuyang Hong¹, Yan Feng¹

¹Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.

Plos Computational Biology

|November 10, 2025

Summary

This summary is machine-generated.

We developed PaaSc, a new computational method to analyze pathway activity in single cells. This tool helps understand cellular heterogeneity and disease mechanisms from complex transcriptomic data.

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Area of Science:

Single-cell and spatial transcriptomics
Computational biology
Systems biology

Background:

Single-cell and spatial transcriptomics offer unprecedented insights into cellular heterogeneity.
Translating high-dimensional transcriptomic data into functional pathway insights remains a significant challenge.
Existing methods struggle to accurately infer pathway activity at single-cell resolution.

Purpose of the Study:

To develop a computational method, PaaSc (Pathway activity analysis of Single-cell), for inferring pathway activity at single-cell resolution.
To enable a deeper understanding of cellular heterogeneity, dynamics, aging, and disease mechanisms.
To provide a robust tool for analyzing diverse transcriptomic data modalities.

Main Methods:

PaaSc utilizes multiple correspondence analysis to project cells and genes into a shared latent space.
Pathway activity scores are inferred by selecting pathway-associated dimensions via linear regression.
Validation was performed on diverse benchmarking datasets, including multi-omic and large-scale cancer scRNA-seq data.

Main Results:

PaaSc demonstrated superior performance compared to state-of-the-art methods in various applications.
The method accurately scored cell type-specific gene sets and identified senescence-associated pathways.
PaaSc showed robust performance across different data modalities (scRNA-seq, scATAC-seq, spatial transcriptomics) and maintained accuracy despite batch effects.

Conclusions:

PaaSc accurately captures dynamic cellular states and spatial patterns, advancing the analysis of transcriptomic data.
The method provides a powerful approach for inferring pathway activity, crucial for understanding cellular dynamics and disease.
PaaSc facilitates deeper insights into aging and complex disease mechanisms by dissecting cellular heterogeneity.