Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq Data

Abstract

The rapid accumulation of single-cell atlases has yielded datasets of unprecedented scale, encompassing samples across diverse platforms, locations and laboratories. This multidimensional complexity drives an urgent need for universal domain adaptation methods capable of achieving precise cell-type annotation. However, existing methods lack computational scalability and fail to integrate biological priors. Here, we develop scPathOT, a pathway-informed universal domain adaptation framework that leverages pathway activation transformations to harmonize single-cell datasets across disparate conditions. We demonstrate the versatility of scPathOT across diverse technological platforms, tissues, disease contexts, cellular senescence and treatment conditions. Crucially, this pathway-informed alignment not only accurately resolves cellular identities but also uncovers functional mechanism. In pancreatic islets, scPathOT delineates a shared stress-repair axis traversed by {beta}-cells prior to their divergence into type 1 and type 2 diabetes-specific states. In aging bone marrow, scPathOT disentangles lineage-specific senescence modules that unexpectedly converge onto unified inflammatory and oxidative-stress programs. Furthermore, application to an in-house pancreatic ductal adenocarcinoma cohort uncovers the mechanistic basis underlying the neoadjuvant chemotherapy-induced reorganization of stromal-immune crosstalk. By coupling biological priors with universal domain adaptation, scPathOT provides a scalable, mechanistically interpretable framework to accelerate biological discovery from atlas-level single-cell data.