Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution

Abstract

Kompot is a statistical framework for holistic comparison of multi-condition single-cell datasets, supporting both differential abundance and differential expression. Differential abundance captures changes in how cells populate the phenotypic manifold across conditions, while differential expression identifies condition-specific changes in gene regulation that may be localized to particular regions of that manifold. Kompot models the distribution of cells and gene expression as continuous functions over a low-dimensional representation of cell states, enabling single-cell resolution inference with calibrated uncertainty estimates. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. By capturing both global and cell-state specific effects of perturbation, Kompot reveals how aging reshapes cellular identity and regulatory programs across the hematopoietic landscape. This framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes.