qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.
Bioinformatics
; 36(20): 5086-5092, 2020 12 22.
Article
em En
| MEDLINE
| ID: mdl-32663244
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.
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Base de dados:
MEDLINE
Assunto principal:
Software
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Finlândia