qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets - Häkkinen A; Koiranen J; Casado J; Kaipio K; Lehtonen O; Petrucci E; Hynninen J; Hietanen S; Carpén O; Pasquini L; Biffoni M; Lehtonen R; et al. (United Kingdom, 2022)
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.