LAUSR

Dimensionality reduction using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm has emerged as a popular tool for visualizing high-parameter single-cell data. While this approach has obvious potential for data visualization it remains unclear how t-SNE analysis compares to conventional manual hand-gating in stratifying and quantitating the frequency of diverse immune cell populations. We applied a comprehensive 38-parameter mass cytometry panel to human blood and compared the frequencies of 28 immune cell subsets using both conventional bivariate and t-SNE-guided manual gating. t-SNE analysis was capable of stratifying every general cellular lineage and most sub-lineages with high correlation between conventional and t-SNE-guided cell frequency calculations. However, specific immune cell subsets delineated by the manual gating of continuous variables were not fully separated in t-SNE space thus causing discrepancies in subset identification and quantification between these analytical approaches. Overall, these studies highlight the consistency between t-SNE and conventional hand-gating in stratifying general immune cell lineages while demonstrating that particular cell subsets defined by conventional manual gating may be intermingled in t-SNE space.