Department of Statistical Science, Southern Methodist University, Dallas, TX, 75275, USA.
Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Nat Commun. 2023 Apr 1;14(1):1836. doi: 10.1038/s41467-023-37478-w.
While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.
虽然单细胞测序(scRNA-seq)的实验和信息学技术已经很先进,但质谱流式细胞术(CyTOF)数据分析的研究却严重滞后。CyTOF 数据在许多方面明显不同于 scRNA-seq 数据。这就需要评估和开发专门针对 CyTOF 数据的计算方法。降维(DR)是单细胞数据分析的关键步骤之一。在这里,我们在 110 个真实和 425 个合成 CyTOF 样本上对 21 种 DR 方法的性能进行了基准测试。我们发现,像 SAUCIE、SQuaD-MDS 和 scvis 这样不太知名的方法总体表现最好。特别是,SAUCIE 和 scvis 表现均衡,SQuaD-MDS 在结构保持方面表现出色,而 UMAP 具有出色的下游分析性能。我们还发现 t-SNE(以及 SQuad-MDS/t-SNE Hybrid)具有最佳的局部结构保持能力。然而,这些工具之间具有高度的互补性,因此方法的选择应取决于基础数据结构和分析需求。