We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The differentially expressed feature genes discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterized by the feature genes as markers. Benchmarking using expert-annotated scRNA-seq datasets shows that our method automatically identifies the 'ground truth' cell assignments with high accuracy.

中文翻译：

---

从单细胞基因表达数据发现推定细胞类型。

我们提出了单细胞聚类评估框架，这是一种从单细胞RNA测序（scRNA-seq）数据自动识别推定细胞类型的方法。通过将机器学习方法迭代应用于给定的一组细胞，我们可以同时识别出不同的细胞组以及每个组的特征基因的加权列表。差异表达的特征基因将给定的细胞群与其他细胞区分开。每个这样的细胞组对应于假定的细胞类型或状态，其特征是特征基因作为标记。使用专家注释的scRNA-seq数据集进行基准测试表明，我们的方法可以自动准确地识别“地面真相”细胞分配。