Wei Xiaopeng, Wu Jingli, Li Gaoshi, Liu Jiafei, Wu Xi, He Chang
Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.
College of Computer Science and Engineering, Guangxi Normal University, Guilin, Guangxi, China.
PLoS Comput Biol. 2025 Apr 28;21(4):e1012924. doi: 10.1371/journal.pcbi.1012924. eCollection 2025 Apr.
It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of scRNA-seq data. In this study, scPEDSSC, a deep sparse subspace clustering method based on proximity enhancement, is put forward. The self-expression matrix (SEM), learned from the deep auto-encoder with two part generalized gamma (TPGG) distribution, are adopted to generate the similarity matrix along with its second power. Compared with eight state-of-the-art single-cell clustering methods on twelve real biological datasets, the proposed method scPEDSSC can achieve superior performance in most datasets, which has been verified through a number of experiments.
通过对单细胞RNA测序（scRNA-seq）数据进行聚类来识别细胞类型是单细胞分析中的重要一步。然而，由于scRNA-seq数据固有的高维性、噪声和稀疏性，仍然存在巨大挑战。在本研究中，提出了一种基于邻近增强的深度稀疏子空间聚类方法scPEDSSC。从具有两部分广义伽马（TPGG）分布的深度自动编码器中学习到的自表达矩阵（SEM），与它的二次幂一起用于生成相似性矩阵。与12个真实生物学数据集上的8种最先进的单细胞聚类方法相比，所提出的scPEDSSC方法在大多数数据集中都能实现卓越性能，这已通过大量实验得到验证。
