# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "snifter" in publications use:' type: software license: GPL-3.0-only title: 'snifter: R wrapper for the python openTSNE library' version: 1.15.0 doi: 10.1101/731877 abstract: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . authors: - family-names: O'Callaghan given-names: Alan email: alan.ocallaghan@outlook.com - family-names: Lun given-names: Aaron preferred-citation: type: article title: 'openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding' authors: - family-names: Poli\v car given-names: Pavlin G. - family-names: Stra\v zar given-names: Martin - family-names: Zupan given-names: Bla\v z year: '2019' doi: 10.1101/731877 publisher: name: Cold Spring Harbor Laboratory url: https://www.biorxiv.org/content/early/2019/08/13/731877 journal: bioRxiv repository: https://bioc.r-universe.dev repository-code: https://github.com/Alanocallaghan/snifter url: https://bioconductor.org/packages/snifter date-released: '2023-09-03' contact: - family-names: O'Callaghan given-names: Alan email: alan.ocallaghan@outlook.com references: - type: article title: Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data authors: - family-names: Linderman given-names: George C. - family-names: Rachh given-names: Manas - family-names: Hoskins given-names: Jeremy G. - family-names: Steinerberger given-names: Stefan - family-names: Kluger given-names: Yuval year: '2019' doi: 10.1038/s41592-018-0308-4 url: https://www.nature.com/articles/s41592-018-0308-4 journal: Nature Methods - type: article title: Accelerating t-SNE using Tree-Based Algorithms authors: - family-names: Maaten given-names: Laurens name-particle: van der journal: Journal of Machine Learning Research year: '2014' volume: '15' issue: '93' url: http://jmlr.org/papers/v15/vandermaaten14a.html start: 3221-3245