A number of specialized clustering methods have been developed so far for the accurate analysis of single-cell RNA-sequencing (scRNA-seq) expression data, and several reports have been published documenting the performance measures of these clustering methods under different conditions. However, to date, there are no available studies regarding the systematic evaluation of the performance measures of the clustering methods taking into consideration the sample size and cell composition of a given scRNA-seq dataset. Herein, a comprehensive performance evaluation study of 11 selected scRNA-seq clustering methods was performed using synthetic datasets with known sample sizes and number of subpopulations, as well as varying levels of transcriptome complexity. The results indicate that the overall performance of the clustering methods under study are highly dependent on the sample size and complexity of the scRNA-seq dataset. In most of the cases, better clustering performances were obtained as the number of cells in a given expression dataset was increased. The findings of this study also highlight the importance of sample size for the successful detection of rare cell subpopulations with an appropriate clustering tool.

中文翻译：

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单细胞RNA测序表达数据的聚类方法：使用不同样本量和细胞组成的性能评估。

迄今为止，已经开发了许多专门的聚类方法来准确分析单细胞RNA测序（scRNA-seq）表达数据，并且已经发表了一些报告，记录了这些聚类方法在不同条件下的性能指标。但是，迄今为止，尚无关于将聚类方法的性能指标进行系统评价的研究，该研究考虑了给定scRNA-seq数据集的样本大小和细胞组成。在本文中，使用合成数据集对11种选定的scRNA-seq聚类方法进行了全面的性能评估研究，该合成数据集具有已知的样本大小和亚种群数量以及不同水平的转录组复杂性。结果表明，正在研究的聚类方法的整体性能高度依赖于样本大小和scRNA-seq数据集的复杂性。在大多数情况下，随着给定表达数据集中细胞数量的增加，可以获得更好的聚类性能。这项研究的结果也突出了使用适当的聚类工具成功检测稀有细胞亚群的样本量的重要性。