Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA.
Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
Genome Biol. 2021 Aug 19;22(1):232. doi: 10.1186/s13059-021-02445-5.
Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.
单细胞 RNA 测序 (scRNA-seq) 为描述细胞群体提供了新的机会，通常通过某种聚类分析来实现。估计最佳聚类数 (K) 是一个关键步骤，但往往被忽略。我们的方法通过提供多分辨率视角来客观估计群组数量，从而改进了当前大多数 scRNA-seq 聚类方法。MultiK 是一种工具，用于通过共识聚类方法客观选择有见地的 K 值，并通过高度稳健性实现。我们证明 MultiK 可以在 scRNA-seq 数据中识别可重复的组，从而为估计可能存在的组或细胞类型群体的数量提供了客观手段。
