Measure transport methods offer a unified framework for processing and analyzing data distributed according to various probability measures, with applications in biological sciences.
Kernel-based testing reveals hidden patterns in single-cell data, enabling a deeper understanding of cell population heterogeneities.
The authors present a spectral manifold alignment and inference (SMAI) framework that enables principled and interpretable alignability testing and structure-preserving integration of single-cell data.