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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 .

SUBMITTER: Linderman GC

PROVIDER: S-EPMC6402590 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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

Linderman George C GC Rachh Manas M Hoskins Jeremy G JG Steinerberger Stefan S Kluger Yuval Y

Nature methods 20190211 3

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 availa ...[more]

PMID: 30742040

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