CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification.

ABSTRACT: Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster

SUBMITTER: Liu J 

PROVIDER: S-EPMC10749894 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature