Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.质谱流式细胞术（Mass Cytometry, CyTOF）极大拓展了流式细胞术的应用能力。当前单项研究中可便捷生成多份CyTOF样本，每份样本可对数十万级别的单个细胞开展50种标志物的单细胞检测。现有方法未能充分解决多样本整合以实现细胞亚群鉴定的相关难题，且随着样本量增加，这类难题会快速显著加剧。为克服该局限，本研究开发了分区辅助聚类与网络多重对齐算法（Partition-Assisted Clustering and Multiple Alignments of Networks, PAC-MAN），可在CyTOF数据中快速自动鉴定细胞群，其结果与专家手动鉴定的结果高度吻合；同时可完成跨样本亚群的对齐分析，以界定数据集层面的细胞状态。PAC-MAN计算效率优异，可处理超大规模CyTOF数据集——这类数据集在针对每位受试者监测多种组织样本的临床研究与癌症研究中愈发常见。