Li Jia, Shyr Yu, Liu Qi
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
Genome Biol. 2024 Aug 1;25(1):203. doi: 10.1186/s13059-024-03339-y.
Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.
用于单细胞和空间转录组学的典型聚类方法难以识别稀有细胞类型，而专门用于检测稀有细胞类型的方法虽具备此能力，但在对丰富细胞类型进行分组时性能较差。在此，我们开发了一种自适应k近邻图优化的KNNO，以同时识别丰富和稀有细胞类型。在38个模拟数据集以及20个单细胞和空间转录组学数据集上进行的基准测试表明，与通用方法和专门方法相比，aKNNO能更准确地识别丰富和稀有细胞类型。仅使用基因表达时，与整合方法相比，aKNNO能更精确地映射丰富和稀有细胞。
