We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.
- Authors
Häkkinen, Antti; Koiranen, Juha; Casado, Julia; Kaipio, Katja; Lehtonen, Oskari; Petrucci, Eleonora; Hynninen, Johanna; Hietanen, Sakari; Carpén, Olli; Pasquini, Luca; Biffoni, Mauro; Lehtonen, Rainer; Hautaniemi, Sampsa
- Abstract
Motivation Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. Results We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. Availability and implementation Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
SOURCE code; QUASI-Newton methods; CYTOMETRY
- Publication
Bioinformatics, 2020, Vol 36, Issue 20, p5086
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btaa637