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, 37232, USA.
bioRxiv. 2023 Oct 17:2023.10.13.562261. doi: 10.1101/2023.10.13.562261.
Single-cell and spatial transcriptomics have been widely used to characterize cellular landscape in complex tissues. To understand cellular heterogeneity, one essential step is to define cell types through unsupervised clustering. While typical clustering methods have difficulty in identifying rare cell types, approaches specifically tailored to detect rare cell types gain their ability at the cost of poorer performance for grouping abundant ones. Here, we developed aKNNO, a method to identify abundant and rare cell types simultaneously based on an adaptive k-nearest neighbor graph with optimization. Benchmarked on 38 simulated and 20 single-cell and spatial transcriptomics datasets, aKNNO identified both abundant and rare cell types accurately. Without sacrificing performance for clustering abundant cell types, aKNNO discovered known and novel rare cell types that those typical and even specifically tailored methods failed to detect. aKNNO, using transcriptome alone, stereotyped fine-grained anatomical structures more precisely than those integrative approaches combining expression with spatial locations and histology image.
单细胞和空间转录组学已被广泛用于表征复杂组织中的细胞景观。为了理解细胞异质性，一个关键步骤是通过无监督聚类来定义细胞类型。虽然典型的聚类方法在识别稀有细胞类型方面存在困难，但专门为检测稀有细胞类型量身定制的方法在识别丰富细胞类型时性能较差。在这里，我们开发了aKNNO，一种基于具有优化的自适应k近邻图同时识别丰富和稀有细胞类型的方法。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基准测试，aKNNO能够准确识别丰富和稀有细胞类型。在不牺牲对丰富细胞类型聚类性能的情况下，aKNNO发现了典型方法甚至专门定制的方法都未能检测到的已知和新型稀有细胞类型。仅使用转录组，aKNNO比那些将表达与空间位置和组织学图像相结合的综合方法更精确地描绘了细粒度的解剖结构。
