In single-cell data analysis, the most critical step can be said to be the result of cell clustering; and cell clustering involves the design of many mathematical models, whether it is high-dimensional data conversion (dimensionality reduction), noise removal (denoising) and extraction Core information, etc., will affect the final cell clustering results, and therefore there are continuous methods to innovate in order to truly show the clustering distribution of cells. PHATE (Potential of Heat diffusion for Affinity-based Transition Embedding) [1] is a new way of dimensionality reduction. Before introducing this new method, the editor would like to review the past list with you The common dimensionality reduction methods of cell analysis…