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• W2897799779 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." @default.
• W2897799779 created "2018-10-26" @default.
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• W2897799779 date "2018-10-24" @default.
• W2897799779 modified "2023-10-11" @default.
• W2897799779 title "Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets" @default.
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• W2897799779 doi "https://doi.org/10.1101/451690" @default.
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