Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hiera

SUBMITTER: Lei T 

PROVIDER: S-EPMC10539043 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature