School of Life Sciences, BIOPIC and Beijing Advanced Innovation Centre for Genomics, Peking University, Beijing, China.
Peking-Tsinghua Centre for Life Sciences, Peking University, Beijing, China.
Nat Commun. 2020 Jun 22;11(1):3155. doi: 10.1038/s41467-020-16904-3.
Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.
单细胞 RNA 测序 (scRNA-seq) 是一种通用的工具，可用于发现和注释细胞类型和状态，但细胞亚型的确定和注释往往是主观和任意的。通常，甚至不清楚给定的簇是否均匀。在这里，我们提出了一种基于熵的统计量 ROGUE，以准确量化鉴定的细胞簇的纯度。我们证明，我们的 ROGUE 指标具有广泛的适用性，并能够在广泛的模拟和真实数据集上进行准确、敏感和稳健的聚类纯度评估。将该指标应用于成纤维细胞、B 细胞和大脑数据，我们鉴定了额外的亚型，并展示了 ROGUE 指导分析在特定亚群中检测精确信号的应用。ROGUE 可应用于所有测试的 scRNA-seq 数据集，对于评估假定簇的质量、发现纯细胞亚型以及构建全面、详细和标准化的单细胞图谱具有重要意义。
