The analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.

单细胞RNA测序数据的分析在健康研究中非常重要。它挑战了数据科学家，但在个性化医学方面具有巨大潜力。单个细胞的聚类旨在以数据驱动的方式检测患者体内不同的细胞群亚群。一些比较研究表明，Kiselev等人提出了单细胞共识聚类（SC3）。（Nat Methods 14（5）：483–486，2017），是对单细胞RNA测序数据进行分类的最佳方法。SC3包括拉普拉斯特征图和主成分分析（PCA）。我们的无监督自适应单细胞共识聚类（adaSC3）建议建议用扩散图代替线性PCA，这是一种考虑了单个细胞过渡的非线性方法。我们根据adaSC3原始来源的数据集的准确性以及模拟研究来研究adaSC 3的性能。的比较adaSC 3与SC3以及与基础上，进一步的替代降维的技术示出的一个很有说服力行为相关算法adaSC 3。