A Hashing-Based Framework for Enhancing Cluster Delineation of High-Dimensional Single-Cell Profiles.

ABSTRACT: Although many methods have been developed to explore the function of cells by clustering high-dimensional (HD) single-cell omics data, the inconspicuously differential expressions of biomarkers of proteins or genes across all cells disturb the cell cluster delineation and downstream analysis. Here, we introduce a hashing-based framework to improve the delineation of cell clusters, which is based on the hypothesis that one variable with no significant differences can be decomposed into more diversely latent variables to distinguish cells. By projecting the original data into a sparse HD space, fly and densefly hashing preprocessing retain the local structure of data, and improve the cluster delineation of existing clustering methods, such as PhenoGraph. Moreover, the analyses on mass cytome

SUBMITTER: Liu X 

PROVIDER: S-EPMC9590516 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature