Antti Häkkinen1, Juha Koiranen1, Julia Casado1, Katja Kaipio2, Oskari Lehtonen1, Eleonora Petrucci3, Johanna Hynninen4, Sakari Hietanen4, Olli Carpén1,2,5, Luca Pasquini6, Mauro Biffoni3, Rainer Lehtonen1, Sampsa Hautaniemi1. 1. Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland. 2. Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, Turku 20014, Finland. 3. Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome 00161, Italy. 4. Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku 20521, Finland. 5. Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital, Helsinki 00014, Finland. 6. Major Equipments and Core Facilities, Istituto Superiore di Sanità, Rome 00161, Italy.
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 downsampling. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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