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

## TL;DR

PaaSc is a new computational method that helps scientists understand which biological processes are active in individual cells using advanced data techniques.

## Contribution

PaaSc introduces a novel approach using multiple correspondence analysis and linear regression to infer pathway activity at single-cell resolution.

## Key findings

- PaaSc outperforms existing methods in scoring cell type-specific gene sets and identifying cell senescence-associated pathways.
- The method maintains accuracy across different data types and is robust to batch effects.
- PaaSc captures dynamic cellular states and spatial patterns, aiding in understanding cellular dynamics and disease mechanisms.

## Abstract

Recent advances in single-cell and spatial transcriptomics have revolutionized our understanding of cellular heterogeneity. However, translating high-dimensional data into functional pathway insights remains challenging. To address this obstacle, we developed PaaSc (Pathway activity analysis of Single-cell), a computational method for inferring pathway activity at single-cell resolution. PaaSc employs multiple correspondence analysis to simultaneously project cells and genes into a common latent space and selects pathway-associated dimensions through linear regression to infer pathway activity scores. We validated PaaSc across diverse benchmarking datasets, including those that jointly profiled protein and RNA levels, as well as large-scale cancer scRNA-seq cohorts. Compared with state-of-the-art methods, PaaSc demonstrated superior performance in multiple applications: scoring cell type-specific gene sets, identifying cell senescence-associated pathways, and exploring GWAS trait-associated cell types. Importantly, PaaSc maintained accuracy despite batch effects and demonstrated robust performance across different data modalities, including scATAC-seq and spatial transcriptomics data. Our results demonstrate that PaaSc accurately captures dynamic cellular states and spatial patterns, thereby advancing our understanding of cellular dynamics, aging, and disease mechanisms.

Understanding how individual cells function and communicate is crucial for advancing medicine and biology. Recent technologies allow scientists to study thousands of cells individually, revealing that even cells of the same type can behave very differently from one another. However, making sense of this overwhelming amount of data remains a major challenge, particularly when attempting to understand which biological processes are active in each cell. We developed a new computational approach called PaaSc to solve this problem. Our method involves analyzing the activity levels of biological pathways—coordinated sets of genes that work together to perform specific cellular functions—in individual cells. Biological pathways can be viewed as different departments in a company: some are responsible for energy production, others manage cell division, and yet others respond to stress. We tested our approach using multiple datasets and reported that it outperforms existing methods across various applications, including identifying cell types, detecting cellular aging, and understanding disease-related processes. Importantly, our method works reliably even when data come from different laboratories or experiments. We believe that this tool will help researchers better understand how cells behave in health and disease, potentially leading to new therapeutic strategies.