ArticlePLoS computational biology2024
The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference.PLoS computational biology · 2026Article
- Interactive visualization of metric distortion in nonlinear data embeddings using the distortions package.Briefings in bioinformatics · 2026Article
- Single cell snapshot analyses under proper representation reveal that epithelial-mesenchymal transition couples at G1 and G2/M.Communications biology · 2026Article
- Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models.Biostatistics (Oxford, England) · 2025Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- On metrics for subpopulation detection in single-cell and spatial omics data.Nucleic acids research · 2025Article
- SuperGLUE facilitates an explainable training framework for multi-modal data analysis.Cell reports methods · 2025Article
- Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.PLoS computational biology · 2025Article
- shinyUMAP: an online tool for promoting understanding of single cell omics data visualization.bioRxiv : the preprint server for biology · 2025Article
- Sketching T cell atlases in the single-cell era: challenges and recommendations.Immunology and cell biology · 2025Review
- Understanding rheumatic disease through continuous cell state analysis.Nature reviews. Rheumatology · 2025Review
- A Hands-On Introduction to Data Analytics for Biomedical Research.Function (Oxford, England) · 2025Review
- Two-by-two ordinal patterns in art paintings.PNAS nexus · 2025Article
- Principled PCA separates signal from noise in omics count data.bioRxiv : the preprint server for biology · 2025Article
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- Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST.Bioinformatics (Oxford, England) · 2024Article
- Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation.bioRxiv : the preprint server for biology · 2024Article
- The State of Single-Cell Atlas Data Visualization in the Biological Literature.IEEE computer graphics and applicationsArticle
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Abstract
A recent paper claimed that t-SNE and UMAP embeddings of single-cell datasets are "specious" and fail to capture true biological structure. The authors argued that such embeddings are as arbitrary and as misleading as forcing the data into an elephant shape. Here we show that this conclusion was based on inadequate and limited metrics of embedding quality. More appropriate metrics quantifying neighborhood and class preservation reveal the elephant in the room: while t-SNE and UMAP embeddings of single-cell data do not preserve high-dimensional distances, they can nevertheless provide biologically relevant information.
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