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Nat Methods. 2019 Mar;16(3):243-245. doi: 10.1038/s41592-018-0308-4. Epub 2019 Feb 11.

Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data.

Author information

1
Applied Mathematics Program, Yale University, New Haven, CT, USA.
2
Department of Mathematics, Yale University, New Haven, CT, USA.
3
Applied Mathematics Program, Yale University, New Haven, CT, USA. yuval.kluger@yale.edu.
4
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. yuval.kluger@yale.edu.

Abstract

t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps .

PMID:
30742040
PMCID:
PMC6402590
[Available on 2019-08-11]
DOI:
10.1038/s41592-018-0308-4

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