Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets
By Anna C. Belkina, Chris Ciccolella, R. Anno, Richard L. Halpert, Josef Spidlen, J. Snyder-Cappione. 2018
18 citations recorded by the source corpus.
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 optimal 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 optimal data resolution in t-SNE space and more precise data interpretation.
Subfields: Biophysics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Molecular Biology
- Dmitry Kobak
- George C. Linderman
- Philipp Berens
- Yuval Kluger
- Stefan Steinerberger
- J. Böhm
- Haiyang Huang
- Cynthia Rudin
Citations in the corpus
- The art of using t-SNE for single-cell transcriptomics (2018, background): “there was a continuous progression from radial glia (cluster 7), to neural progenitors (9), to postmitotic premature neurons (10), to mature excitatory (5) and inhibitory (15) neurons.”
- SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, methodology): “For example, the widely used tSNE [24] has formed the basis 30 for faster ASNE [17] and HSNE [16], and was further improved in OptSNE and FItSNE [3, 14] and accelerated using GPU by Chan et al.”
- Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations (2019, background): “In the cytometric context, this phenomenon was described in detail by [2].”
- Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda (2020, methodology): “Rather, neighboring points in the input space should tend to be neighbors in the lowdimensional space, so we use the relative distances (or their absence) between lowdimensional points for the validation (Belkina et al., 2018; Chan et al., 2019).”