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bioRxiv · 10.1101/451690

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

Belkina, A. C.·Ciccolella, C. O.·Anno, R.·Spidlen, J.·Halpert, R.·Snyder-Cappione, J.
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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 developed opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Liebler 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.

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Belkina, A. C., Ciccolella, C. O., Anno, R., Spidlen, J., Halpert, R., Snyder-Cappione, J.. 2018-10-24. Automated optimal parameters for T-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets. https://doi.org/10.1101/451690

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