ArticleNature methods2019
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 216 papers, 1 of them a synthesis that pooled it.
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Who cites it
216 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Protocol for generating high-quality, 45-color spectral flow cytometry data for unsupervised clustering to investigate aging in human PBMCs.STAR protocols · 2026Article
- Development and validation of a TLS-associated signature for prognosis prediction in breast cancer: new insights into QPRT.Frontiers in immunology · 2026Article
- Single-Cell Transcriptomics Reveals Dynamic Cellular Interactions and Molecular Mechanisms in Myocardial Infarction Recovery.International journal of genomics · 2026Article
- ICAM signaling rewires the inflammatory communication network in pediatric gout.Frontiers in immunology · 2026Article
- Single-cell Unperturbed Activation Profiling by mass cytometry capturesFrontiers in immunology · 2026Article
- Mapping the complexity of ME/CFS: Evidence for abnormal energy metabolism, altered immune profile, and vascular dysfunction.Cell reports. Medicine · 2025Article
- TScience advances · 2025Article
- Ex vivo lung-organoid model for aberrant basaloid cell induction and activation.Inflammation and regeneration · 2025Article
- A clinically annotated transcriptomic atlas of nervous system tumors.Neuro-oncology · 2025Article
- Unsupervised electric signal separation for linking behavior and electrocommunication in Gnathonemus petersii.Scientific reports · 2025Article
- Mucosal vaccination with long-form TSLP induces migratory cDC1-mediated adaptive immunity against SARS-CoV-2 infection.Journal of virology · 2025Article
156 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.