Abstract
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.
Highlights
Analyses of CyTOF data rely on many of the tools and ideas from flow cytometry (FC) data analysis, as CyTOF datasets are essentially higher dimensional versions of flow cytometry datasets
We introduce Multiple Alignments of Networks to resolve the management issue surrounding the organization of homogeneous clusters found in the partition-assisted clustering (PAC) step (Fig 5)
The network alignment is more stringent in the establishment of linkages; the network Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-Multiple Alignments of Networks (MAN)) approach defines cellular states with the additional information from network structures, and it has the effect of constraining the number of linkages between samples while finding linkages for subpopulations that are distant in their means
Summary
The cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). It is feasible to collect more samples, which enables systematic studies of cell types across multiple samples. The statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. It was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. Scalable multi-sample analysis of single-cell data data collection and analysis, decision to publish, or preparation of the manuscript. PAC-MAN enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of large or larger datasets
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