High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell’s heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can not only segment multiple linear subspaces, but also extract important information. Moreover, adaptive total variation also can remove cell noise and preserve cell feature details by learning the gradient information of the data. At the same time, to analyze scRNA-seq data with unknown prior information, we introduced the maximum eigenvalue method into the ATV-LRR model to automatically identify cell populations. The final clustering results show that the ATV-LRR model can detect cell types more effectively and stably.

单细胞基因表达的高通量测序揭示了群体中单个细胞异质性的复杂机制。分析单细胞 RNA 测序 (scRNA-seq) 数据的一个重要目的是通过细胞聚类识别细胞亚型和功能。为了处理高水平的噪声和细胞异质性，我们引入了一种新的单细胞数据分析模型，称为自适应总变异正则化低秩表示 (ATV-LRR)。在scRNA-seq数据中，ATV-LRR可以重构低秩子空间结构来学习细胞的相似性。低秩表示不仅可以分割多个线性子空间，还可以提取重要信息。此外，自适应总变异还可以通过学习数据的梯度信息来去除细胞噪声并保留细胞特征细节。同时，为了分析先验信息未知的scRNA-seq数据，我们在ATV-LRR模型中引入了最大特征值法来自动识别细胞群。最终的聚类结果表明，ATV-LRR模型可以更有效、更稳定地检测细胞类型。