While traditional biaxial data presentation via expert-driven gating is still the standard analysis method for cytometry data, with the advent of the modern multi-parameter era an analysis tool that can accurately and comprehensively visualize multi-dimensional data is direly needed to relieve the current cytometry data-processing bottleneck. Fluorescence, mass and sequencing-based cytometric data analysis requires tools that are able to reveal the combinations of proteomic and/or transcriptomic markers that define complex and diverse cell phenotypes in a mixed population. Visual exploration of high-dimensional data is imperative for the comprehensive analysis of single cell datasets. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation. 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.
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. 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. Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations.
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