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Full-Text Articles in Medicine and Health Sciences
Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen
Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen
Faculty Scholarship for the College of Science & Mathematics
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. …