RT Journal Article
SR Electronic
T1 openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 731877
DO 10.1101/731877
A1 Pavlin G. Poličar
A1 Martin Stražar
A1 Blaž Zupan
YR 2019
UL http://biorxiv.org/content/early/2019/08/13/731877.abstract
AB 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{at}fri.uni-lj.si, blaz.zupan{at}fri.uni-lj.si