IEEE J Biomed Health Inform. 2021 Jan;25(1):247-256. doi: 10.1109/JBHI.2020.2991172. Epub 2021 Jan 5.
Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR).
单细胞 RNA 转录组数据为研究细胞异质性提供了巨大的机会。基于 scRNA-seq 数据识别亚群是近年来的一个热门话题，尽管许多研究人员一直专注于设计用于识别新细胞类型的优雅计算方法；然而，由于 scRNA-seq 数据的高维性、稀疏性和噪声，这些方法的性能仍然不尽如人意。在这项研究中，我们提出了一种通过学习稳健准确的相似性矩阵来检测新细胞类型的方法，称为 SCCLRR。该方法基于低秩表示 (LRR) 框架数学模型，同时捕捉数据的全局和局部内在特性。综合归一化欧几里得距离和余弦相似度用于平衡局部正则化项中数据的内在线性和非线性流形。为了解决非凸优化模型，我们使用交替方向乘子法 (ADMM) 算法提出了一种迭代优化过程。我们在九个真实的 scRNA-seq 数据集上评估了 SCCLRR 方法的性能，并将其与七种最先进的方法进行了比较。模拟结果表明，SCCLRR 优于其他方法，并且对于聚类 scRNA-seq 数据是稳健和有效的。(SCCLRR 的代码可在学术上免费获得 https://github.com/wzhangwhu/SCCLRR)。
