ArticleNature communications2019
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.
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 221 papers, 1 of them a synthesis that pooled it.
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Who cites it
221 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Coexistent alterations of BAFF and B-cell phenotypes in complicated CVID course.The Journal of allergy and clinical immunology · 2026Article
- A 38-color spectral panel for cost efficient characterization of T cell therapy products.Cytotherapy · 2026Article
- A cuproptosis-related lncRNAs-based risk signature for predicting prognosis and immune status in glioma.Translational cancer research · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Decoding the chemical space of fast-ion conductors via a descriptor-guided transfer learning framework.Science advances · 2026Article
- A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.RSC advances · 2026Article
- Unsupervised Flow Cytometry Reveals a Constant Shift Towards Activated CD4Scandinavian journal of immunology · 2026Article
- SATB1 is a targetable modulator of JAK-STAT signaling and cytokines in human Treg and Tconv cells.EMBO reports · 2026Article
- Cellular immunology data enable clinical severity prediction via supervised machine learning.iScience · 2026Article
- Protocol for metabolic profiling of antigen-specific CD8STAR protocols · 2026Article
- Multimodal Peripheral Blood Immunophenotyping and Single-Cell Epigenetic Analysis of Knee Osteoarthritis Reveals Expansion and Epigenetic Dysregulation of Treg Cell and Nonclassical Monocyte Populations.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- [Patient-specific electroencephalography epileptic seizure prediction method using global dynamic multi-scale spatio-temporal features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Stem-like PD-1Journal for immunotherapy of cancer · 2026Article
- Inflammation rewires the enteric nervous system through neurogenic monocyte recruitment.The Journal of experimental medicine · 2026Article
- High-Dimensional Protein Analysis Uncovers Distinct Immunologic and Stromal Features in Primary and Metastatic Pancreatic Ductal Adenocarcinoma.Cancer research · 2026Article
161 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.
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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.