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Purpose:
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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 more than 40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. Here, we present One-SENSE (One-dimensional Soli-Expression by Nonlinear Stochastic Embedding), a dimensionality reduction method based on the 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 to higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heatplots. 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 to the original t-SNE algorithm and could be useful for any high-dimensional dataset. |
