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

• Published in: PLoS computational biology Vol. 13; no. 12; p. e1005875
• Main Authors: Li, Ye Henry, Li, Dangna, Samusik, Nikolay, Wang, Xiaowei, Guan, Leying, Nolan, Garry P., Wong, Wing Hung
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2017; Public Library of Science (PLoS)
• Subjects: Alignment; Animals; Automation; Bioinformatics; Biology and Life Sciences; Biomarkers - analysis; Cancer; Cell interactions; Cluster Analysis; Clustering; Computational Biology; Computer and Information Sciences; Computer Simulation; Consortia; Cytometry; Data analysis; Data Interpretation, Statistical; Data processing; Databases, Factual; Datasets; Flow cytometry; Flow Cytometry - statistics & numerical data; Gene Expression; Humans; Immunology; Laboratories; Medicine and Health Sciences; Methods; Mice; Observations; Partitions; Physical Sciences; Research and Analysis Methods; Single-Cell Analysis - statistics & numerical data; Software; Studies; Subpopulations; Supervision
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005875

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.