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

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-

SUBMITTER: Liao X 

PROVIDER: S-EPMC12622815 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature