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

• Published in: Nature methods Vol. 16; no. 3; pp. 243 - 245
• Main Authors: Linderman, George C., Rachh, Manas, Hoskins, Jeremy G., Steinerberger, Stefan, Kluger, Yuval
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2019; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/1386; 631/114/794; Algorithms; Animals; Base Sequence; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Brief Communication; Computational Biology - methods; Embedding; Gene expression; Gene Expression Profiling - methods; Gene sequencing; Genetic Markers; Interpolation; Life Sciences; Mice; Proteomics; Ribonucleic acid; RNA; RNA - genetics; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Stochastic Processes; Visualization
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-018-0308-4

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 . FIt-SNE, a sped-up version of t-SNE, enables visualization of rare cell types in large datasets by obviating the need for downsampling. One-dimensional t-SNE heatmaps allow simultaneous visualization of expression patterns from thousands of genes.