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 Methodsjournal_title
Nature methodsauthors
Linderman GC,Rachh M,Hoskins JG,Steinerberger S,Kluger Ydoi
10.1038/s41592-018-0308-4subject
Has Abstractpub_date
2019-03-01 00:00:00pages
243-245issue
3eissn
1548-7091issn
1548-7105pii
10.1038/s41592-018-0308-4journal_volume
16pub_type
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