The development of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) technology has led to great opportunities for the identification of heterogeneous cell types in complex tissues. Clustering algorithms are of great importance to effectively identify different cell types. In addition, the definition of the distance between each two cells is a critical step for most clustering algorithms. In this study, we found that different distance measures have considerably different effects on clustering algorithms. Moreover, there is no specific distance measure that is applicable to all datasets. In this study, we introduce a new single-cell clustering method called SD-h, which generates an applicable distance measure for different kinds of datasets by optimally synthesizing commonly used distance measures. Then, hierarchical clustering is performed based on the new distance measure for more accurate cell-type clustering. SD-h was tested on nine frequently used scRNA-seq datasets and it showed great superiority over almost all the compared leading single-cell clustering algorithms.

中文翻译：

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通过重建细胞间差异对单细胞进行基于共识的聚类

单细胞核糖核酸 (RNA) 测序 (scRNA-seq) 技术的发展为识别复杂组织中的异质细胞类型带来了巨大机遇。聚类算法对于有效识别不同的细胞类型非常重要。此外，定义每两个细胞之间的距离是大多数聚类算法的关键步骤。在这项研究中，我们发现不同的距离度量对聚类算法有相当不同的影响。此外，没有适用于所有数据集的特定距离度量。在这项研究中，我们介绍了一种称为 SD-h 的新单细胞聚类方法，该方法通过优化合成常用距离度量来为不同类型的数据集生成适用的距离度量。然后，基于新的距离度量执行层次聚类，以实现更准确的细胞类型聚类。SD-h 在九个常用的 scRNA-seq 数据集上进行了测试，与几乎所有比较领先的单细胞聚类算法相比，它表现出极大的优势。