NDLI: Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding.

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Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Cheng, Yang Wong, Michael T. Newell, Evan W. Van Der Maaten, Laurens
Description	Author Affiliation: Cheng Y ( Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648); Wong MT ( Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648); van der Maaten L ( Pattern Recognition Laboratory, Delft University of Technology, 2628 CD Delft, the Netherlands.); Newell EW ( Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648)
Abstract	Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry allows for simultaneous measurement of >40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. In this article, we present one-dimensional soli-expression by nonlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. In contrast with higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heat plots. We applied One-SENSE to probe relationships between categories of human T cell phenotypes and observed previously unappreciated cellular populations within an orchestrated view of immune cell diversity. The presentation of high-dimensional cytometric data using One-SENSE showed a significant improvement in distinguished T cell diversity compared with the original t-SNE algorithm and could be useful for any high-dimensional dataset.
ISSN	00221767
e-ISSN	15506606
DOI	10.4049/jimmunol.1501928
Journal	The Journal of Immunology
Issue Number	2
Volume Number	196
Language	English
Publisher	The American Association of Immunologists
Publisher Date	2016-01-15
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Algorithms Flow Cytometry Single-cell Analysis T-lymphocytes Cytology Principal Component Analysis Discipline Immunology
Content Type	Text
Resource Type	Article
Subject	Immunology and Allergy Immunology

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