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 cytometry dataset show that our hashing-based framework manages to unveil new hidden heterogeneities in cell clusters. The proposed framework promotes the utilization of cell biomarkers and enriches the biological findings by introducing more latent variables.