Artikel ; Online: Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.
2019 Band 10, Heft 1, Seite(n) 5415
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 ... ...
| 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. |
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| Mesh-Begriff(e) | Algorithms ; Animals ; Automation ; Computational Biology ; Data Visualization ; Datasets as Topic ; Flow Cytometry ; Gene Expression Profiling ; Humans ; Machine Learning ; Mice ; Nonlinear Dynamics ; Principal Component Analysis |
| Sprache | Englisch |
| Erscheinungsdatum | 2019-11-28 |
| Erscheinungsland | England |
| Dokumenttyp | Journal Article |
| ZDB-ID | 2553671-0 |
| ISSN | 2041-1723 ; 2041-1723 |
| ISSN (online) | 2041-1723 |
| ISSN | 2041-1723 |
| DOI | 10.1038/s41467-019-13055-y |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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