Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Anna C. Belkina (), Christopher O. Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen and Jennifer E. Snyder-Cappione
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Anna C. Belkina: Department of Pathology and Laboratory Medicine, Boston University School of Medicine
Christopher O. Ciccolella: Omiq, Inc
Rina Anno: Kansas State University
Richard Halpert: BD Life Sciences–FlowJo
Josef Spidlen: BD Life Sciences–FlowJo
Jennifer E. Snyder-Cappione: Boston University School of Medicine

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.

Date: 2019
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DOI: 10.1038/s41467-019-13055-y

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