RT Journal Article SR Electronic T1 Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 116566 DO 10.1101/116566 A1 Ye Henry Li A1 Dangna Li A1 Nikolay Samusik A1 Xiaowei Wang A1 Leying Guan A1 Garry P. Nolan A1 Wing Hung Wong YR 2017 UL http://biorxiv.org/content/early/2017/03/13/116566.abstract AB Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF datasets in a single study, with each dataset containing single-cell measurement on 50 markers for hundreds of thousands 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 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.