Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.
细胞类型识别是单细胞分析中的一个重要步骤，是差异表达分析、轨迹推断等下游分析的基础。在本文中，我们将其看作一个无监督聚类问题，提出了一种基于稀疏子空间假设的单细胞聚类算法SSRE。该方法利用相同类型细胞的基因表达能够相互表示的特性构建全局的细胞相似性。考虑到理想情况下，细胞相似性矩阵应该是按细胞类型呈现对角分块且稀疏的，因此SSRE通过L1范数来约束细胞相互表示矩阵的稀疏度，并采用交替方向乘子法(ADMM)求解优化模型。另外，考虑到求解细胞互相表示系数过程，LASSO的特征缩减特性，SSRE利用细胞间的pearson相关性、spearman相关性和余弦相似度三种经典的相似性作为补充信息，对细胞相互表示矩阵进行部分填充增强，由此得到更可靠的细胞相似性。通过对细胞表示矩阵对称化获得细胞相似性矩阵，最后结合谱聚类获得最终的细胞分组。考虑到单细胞转录组数据的高维度、高噪声和高稀疏性的特点，SSRE利用文中的四种相似性设计了一种基于拉普拉斯得分的基因选择策略。在10套真实单细胞转录组数据集和5套模拟数据集上测试SSRE的性能，并选取8个经典的单细胞聚类方法作为比较。实验结果表明，大多数情况下，SSRE有更高的聚类准确度。另外，SSRE可以很容易地扩展到单细胞转录组数据的可视化和差异表达基因的识别上。SSRE的代码可在https://github.com/CSUBioGroup/SSRE获得。





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