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 .
Keywords
Gene Expression
MeSH terms
Algorithms
Animals
Base Sequence
Computational Biology
Gene Expression Profiling
Genetic Markers
Mice
RNA
Sequence Analysis, RNA
Single-Cell Analysis
Stochastic Processes
Authors
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