openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding

Abstract Summary Point-based visualisations of large, multi-dimensional data from molecular biology can reveal meaningful clusters. One of the most popular techniques to construct such visualisations is t-distributed stochastic neighbor embedding (t-SNE), for which a number of extensions have recently been proposed to address issues of scalability and the quality of the resulting visualisations. We introduce openTSNE, a modular Python library that implements the core t-SNE algorithm and its extensions. The library is orders of magnitude faster than existing popular implementations, including those from scikit-learn. Unique to openTSNE is also the mapping of new data to existing embeddings, which can surprisingly assist in solving batch effects. Availability openTSNE is available at https://github.com/pavlin-policar/openTSNE . Contact pavlin.policar@fri.uni-lj.si , blaz.zupan@fri.uni-lj.si