ArticleCell reports methods2023
A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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Who cites it
25 citing papers in PubMed.
- Article
- Th17 effector cytokines induce shared and distinct microglial and endothelial cell responses in a mouse model for post-streptococcal encephalitis.Nature communications · 2026Article
- High-dimensional profiling following third MMR vaccination or mumps virus infection reveals individuals with suboptimal immunity.Communications medicine · 2026Article
- On-demand growth of semiconductor heterostructures guided by physics-informed machine learning.Science advances · 2026Article
- Mortality Patterns and Trends in Fars Province, Iran (2015-2024): A Retrospective Analysis of Death Records.Iranian journal of medical sciences · 2026Article
- Manifold-based learning for high-throughput single-peanut phenotyping.NPJ systems biology and applications · 2026Article
- Th17 effector cytokines induce shared and distinct microglial and endothelial cell responses in post-streptococcal encephalitis.bioRxiv : the preprint server for biology · 2026Article
- Neuroimmune Dysregulation and AI-Driven Therapeutic Strategies in Alzheimer's Disease.Cellular and molecular neurobiology · 2025Review
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma.Biomolecules & biomedicine · 2025Article
- Tracking Oral Nanoparticle Uptake in Mouse Gastrointestinal Tract by Fluorescent Labeling and t-SNE Flow Cytometry.Bio-protocol · 2025Article
- A Gene-Expression Based Comparison of Murine and Human Inhibitory Interneurons in the Cerebellar Cortex and Nuclei.Cerebellum (London, England) · 2025Article
- Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints.Communications physics · 2025Article
- Development of a humanized mouse model of graft-versus-host disease to assess human regulatory T cell function.Frontiers in immunology · 2025Article
- Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation.Sensors (Basel, Switzerland) · 2024Article
- Peripheral endotoxin exposure in mice activates crosstalk between phagocytes in the brain and periphery.Research square · 2024Article
- AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment.SSM - population health · 2024Article
- Article
- Characterization of CD34Cancers · 2024Article
- Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.Frontiers in molecular biosciences · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The advent of high-dimensional single-cell data has necessitated the development of dimensionality-reduction tools. t-Distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are the two most frequently used approaches, allowing clear visualization of complex single-cell datasets. Despite the need for quantitative comparison, t-SNE and UMAP have largely remained visualization tools due to the lack of robust statistical approaches. Here, we have derived a statistical test for evaluating the difference between dimensionality-reduced datasets using the Kolmogorov-Smirnov test on the distributions of cross entropy of single cells within each dataset. As the approach uses the inter-relationship of single cells for comparison, the resulting statistic is robust and capable of identifying true biological variation. Further, the test provides a valid distance between single-cell datasets, allowing the organization of multiple samples into a dendrogram for quantitative comparison of complex datasets. These results demonstrate the largely untapped potential of dimensionality-reduction tools for biomedical data analysis beyond visualization.
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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.