Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

Karthik Shekhar; Petter Brodin; Mark M. DavisArup K. Chakraborty

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Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

机译：Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

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Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8~+ T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8~+ T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.

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《Proceedings of the National Academy of Sciences of the United States of America》 |2014年第1期|202-207|共6页
作者
Karthik Shekhar; Petter Brodin; Mark M. DavisArup K. Chakraborty;
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作者单位

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139;

Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94304;

Ragon Institute of MGH, MIT and Harvard, Boston, MA 02129;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
immunophenotyping; machine learning; class discovery; CyTOF; FACS;

Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

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