Fast Interpolation-based T-SNE for Improved Visualization of Single-cell RNA-seq Data

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2019 Feb 12

PMID 30742040

Citations 171

Authors

George C Linderman

Manas Rachh

Jeremy G Hoskins

Stefan Steinerberger

Yuval Kluger

Affiliations

Soon will be listed here.

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 .

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