title: Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding.
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.
authors.affiliation: Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648; and.
authors.forename: Yang
authors.identifier: http://orcid.org/0000-0002-5906-7694
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648; and.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Wong
authors.affiliation: Pattern Recognition Laboratory, Delft University of Technology, 2628 CD Delft, the Netherlands.
authors.forename: Laurens
authors.initials: L
authors.lastname: van der Maaten
authors.affiliation: Singapore Immunology Network, Agency of Science, Technology and Research, Singapore 138648; and evan_newell@immunol.a-star.edu.sg.
authors.forename: Evan W
authors.identifier: http://orcid.org/0000-0002-2889-243X
authors.initials: EW
authors.lastname: Newell
mesh_terms: D000465:Algorithms; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D059010:Single-Cell Analysis / Q000379:methods*; D013601:T-Lymphocytes / Q000166:cytology*