ArticleNature communications2019
The art of using t-SNE for single-cell transcriptomics.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 466 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
466 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Article
- Analysis of factors driving variations in neonatal parenteral nutrition over a decade: A retrospective study.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026Article
- Navigating Large Chemical Spaces Using Graph Theory and Integer Programming.Journal of chemical information and modeling · 2026Article
- FlowMap: Geometry-Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories.bioRxiv : the preprint server for biology · 2026Article
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Clinical and Inflammatory Phenotypes During Exacerbations Predict In-Hospital Adverse Outcomes in Asthma-COPD Overlap.Respirology (Carlton, Vic.) · 2026Article
- Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.NAR genomics and bioinformatics · 2026Article
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Single-Cell and Machine Learning Analyses Identify a PFKFB3-Centered Regulatory Network and Potential Salidroside Interaction in Coronary Heart Disease.International journal of molecular sciences · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- DCFFNet: A New Dual-channel Cross-Feature Fusion Net for Evaluating the Degree of Aortic Valve Calcification Based on Echocardiographic Images.Journal of imaging informatics in medicine · 2026Article
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- An atlas of primate insular cortex reveals a signal-processing strategy in von Economo neurons.Nature cell biology · 2026Article
- Fast and inexpensive visualization of genome collection at scale.BMC bioinformatics · 2026Article
- Article
- Developmental chronology of mouse embryo from 2-cell stage through birth.Nature cell biology · 2026Article
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- Single-Cell Omics Advances in Understanding Tissue Development and Complex Trait Formation in Sheep and Goats.Animals : an open access journal from MDPI · 2026Review
406 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.
Indexed as
Identifiers
What OpenQuestion holds
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.