PT  - JOURNAL ARTICLE
AU  - Belkina, Anna C.
AU  - Ciccolella, Christopher O.
AU  - Anno, Rina
AU  - Halpert, Richard
AU  - Spidlen, Josef
AU  - Snyder-Cappione, Jennifer E.
TI  - Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets
AID  - 10.1101/451690
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 451690
4099  - http://biorxiv.org/content/early/2019/05/17/451690.short
4100  - http://biorxiv.org/content/early/2019/05/17/451690.full
AB  - 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.