A unified analysis of cell-type- and trajectory-associated pathways in single-cell data using Phoenix

Single-cell RNA sequencing has transformed our ability to resolve complex cellular heterogeneity within biospecimens at the molecular level. However, identifying which biological pathways accurately reflect distinct cell types or continuous cellular trajectories remains a major challenge. Traditional methods often miss subtle or nonlinear pathway activities, limiting biological interpretability and insights. To address this, we develop Phoenix, a pathway analysis framework that leverages random forest models and nonparametric significance testing to evaluate the relevance of functional gene sets for distinguishing between cell types and organizing cells along pseudotemporal cellular trajectories. Phoenix reveals both up- and downregulated processes, including those shaped by complex nonlinear gene interactions, and quantifies their effect sizes. Applied to human and mouse hematopoiesis as well as zebrafish embryogenesis, Phoenix identifies both cell-type-specific and trajectory-associated pathways, spanning housekeeping, developmental, and lineage-specific programs. It outperforms existing tools in capturing cell-type-specific activities of small pathways and reveals greater overlap in pathway activities across species. Ultimately, Phoenix provides a sensitive and interpretable framework for uncovering biologically meaningful pathways and eliciting the interactions between their components in complex single-cell data sets, opening new opportunities to explore dynamic gene regulation across biological systems.