ArticleBriefings in bioinformatics2023
CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.Bioinformatics (Oxford, England) · 2026Article
- Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.Briefings in bioinformatics · 2026Article
- Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework.Briefings in bioinformatics · 2026Article
- scHLens: a web server for hierarchically and interactively exploring single cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation.Briefings in bioinformatics · 2024Article
- Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
- Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).
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