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Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008). Google Scholar Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013). Article PubMed Central ...
Extended Data Fig. 6 Distinct spatial organization and chromatin structure per cell type. a, t-SNE 2D projection of the RNA expression data, clustered with DBSCAN (see Methods). Annotations identified by manual inspection are indicated by matching colours and numbers (labelled on the right). This...
Interactive distortion[133] supports the research process data using distortion scale with partial detail. The basic idea of this method is that a part of the fine granularity displayed data is shown in addition to one with a low level of details. The most popular methods are hyperbolic and sp...
Methods: Using publicly available Medicare Part D claims data, we identified and visualized regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences ...
Coifman, R.R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps.Proc. Natl. Acad. Sci. USA102, 7426–7431 (2005). ArticleCASGoogle Scholar Van Der Maaten, L. & Hinton, G. Visualizing high-dimensional data using t-SNE. journal ...
(email: l.krause@uq.edu.au) Scientific Reports | 6:38178 | DOI: 10.1038/srep38178 1 www.nature.com/scientificreports/ data using these currently available tools is frequently hampered by the advanced computational skills generally required to use them, by their limited selection of statistical ...
Uniform Manifold Approximation and Projection (UMAP) [28] is a state-of-the-art dimensionality reduction technique widely used in machine learning and data analysis. Competing with PCA and t-SNE, UMAP is characterized by its ability to map high-dimensional data to lower-dimensional spaces while ...