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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5760091/ https://www.ncbi.nlm.nih.gov/pubmed/29281633 http://dx.doi.org/10.1371/journal.pcbi.1005875 |
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author | Li, Ye Henry Li, Dangna Samusik, Nikolay Wang, Xiaowei Guan, Leying Nolan, Garry P. Wong, Wing Hung |
author_facet | Li, Ye Henry Li, Dangna Samusik, Nikolay Wang, Xiaowei Guan, Leying Nolan, Garry P. Wong, Wing Hung |
author_sort | Li, Ye Henry |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5760091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57600912018-01-26 Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks Li, Ye Henry Li, Dangna Samusik, Nikolay Wang, Xiaowei Guan, Leying Nolan, Garry P. Wong, Wing Hung PLoS Comput Biol Research Article 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. Public Library of Science 2017-12-27 /pmc/articles/PMC5760091/ /pubmed/29281633 http://dx.doi.org/10.1371/journal.pcbi.1005875 Text en © 2017 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Ye Henry Li, Dangna Samusik, Nikolay Wang, Xiaowei Guan, Leying Nolan, Garry P. Wong, Wing Hung Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title | Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title_full | Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title_fullStr | Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title_full_unstemmed | Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title_short | Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks |
title_sort | scalable multi-sample single-cell data analysis by partition-assisted clustering and multiple alignments of networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5760091/ https://www.ncbi.nlm.nih.gov/pubmed/29281633 http://dx.doi.org/10.1371/journal.pcbi.1005875 |
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