Abstract
Cellular pathways represent complex networks of molecular interactions that regulate biological processes, from signal transduction to metabolic functions. The ability to accurately quantify pathway activity in individual cells within their spatial context represents a crucial step toward understanding cellular behavior, tissue organization, and disease mechanisms. Here, we propose a new method for calculating pathway activity, PaaSc, based on multiple correspondence analysis (MCA) and linear regression. PaaSc can more accurately quantify the activity of a pathway in scRNA-seq and spatial transcriptomics.
Figure 1: PaaSc workflow
Installation
PaaSc is hosted in Github. It can be installed from Github:
install.packages("devtools")
devtools::install_github("yoyoong/PaaSc", ref = "main")Or install from local source:
download.file("https://github.com/yoyoong/PaaSc/releases/download/1.0.0/PaaSc_1.0.0.tar.gz","PaaSc_1.0.0.tar.gz")
install.packages("PaaSc_1.0.0.tar.gz", repos = NULL, type="source")Documentation
- Pathway Activity Analysis for Single Cells Tutorial
- Pathway Activity Analysis for spatial transcriptomics Tutorial
Datasets
Datasets used in this study can be accessible through this link: Datasets
Citation
Xiqi Liao#, Yuyang Hong#, Yan Feng#, Henghui Li, Hai Fang, Jiantao Shi* Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc. PLoS Computational Biology 2025