Journal
CELL SYSTEMS
Volume 7, Issue 2, Pages 185-+Publisher
CELL PRESS
DOI: 10.1016/j.cels.2018.05.017
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Funding
- US NIH [R01GM081871]
- Kwanjeong Educational Foundation
- Sloan Research Fellowship
- US National Science Foundation Career Award [1652815]
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM081871] Funding Source: NIH RePORTER
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Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot be generalized to analyze new datasets. Here we introduce net-SNE, a generalizable visualization approach that trains a neural network to learn a mapping function from high-dimensional single-cell gene-expression profiles to a low-dimensional visualization. We benchmark net-SNE on 13 different datasets, and show that it achieves visualization quality and clustering accuracy comparable with t-SNE. Additionally we show that the mapping function learned by net-SNE can accurately position entire new subtypes of cells from previously unseen datasets and can also be used to reduce the runtime of visualizing 1.3 million cells by 36-fold (from 1.5 days to an hour). Our work provides a framework for boot-strapping single-cell analysis from existing datasets.
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