A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.
visualization machine-learning dimensionality-reduction nearest-neighbors single-cell graph-layout umap diffusion-process denoising high-dimensional
- Updated
Apr 4, 2021 - Python