QSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.

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 d

SUBMITTER: Hakkinen A 

PROVIDER: S-EPMC7755412 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature