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 non-linear pathway activities, limiting biological interpretability and insights. To address this, we developed Phoenix, a pathway analysis framework that leverages random forest models and non-parametric significance testing to evaluate the relevance of functional gene sets for cell-type classification and pseudotemporal cellular trajectories. Phoenix reveals both up- and down-regulated processes, including those shaped by complex non-linear gene interactions, and quantifies their effect sizes. Applied to human and mouse hematopoiesis as well as zebrafish embryogenesis, Phoenix identified both cell-type-specific and trajectory-associated pathways, spanning housekeeping, developmental, and lineage-specific programs. It outperformed existing tools in capturing cell-type-specific activities and revealed greater overlap in pathway activities across species. By integrating statistical rigor with trajectory- and cell-type-aware analysis, Phoenix provides a sensitive, context-driven framework for uncovering biologically meaningful pathways in complex single-cell datasets, opening new opportunities to explore dynamic gene regulation across biological systems.