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

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 .

journal_name

Nat Methods

journal_title

Nature methods

authors

Linderman GC,Rachh M,Hoskins JG,Steinerberger S,Kluger Y

doi

10.1038/s41592-018-0308-4

subject

Has Abstract

pub_date

2019-03-01 00:00:00

pages

243-245

issue

3

eissn

1548-7091

issn

1548-7105

pii

10.1038/s41592-018-0308-4

journal_volume

16

pub_type

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