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UMAP: Uniform Manifold Approximation and Projection
Open accessAbstract
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. UMAP has a rigorous mathematical foundation, but is simple to use, with a scikit-learn compatible API. UMAP is among the fastest manifold learning implementations available -significantly faster than most t-SNE implementations.
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“Other methods, such as t-distributed stochastic neighbor embedding (t-SNE) (68) or uniform manifold approximation and projection (UMAP) (69), aim to preserve apparent pairwise distances approximately in a reduced two-dimensional space so that it can be readily visualized.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [69]
In the citing paper
“Uniform Manifold Approximation and Projection (UMAP) [58] is a successor of t-SNE, superior in run time performance, scalability regarding larger datasets, and perseverance of data structure.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
In the citing paper
“Lack of maturity”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
In the citing paper
“Improved time performance, Better perseverance of data structure”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
Self-citationMcInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22
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Szabo, P. A., Dogra, P., Gray, J. I., Wells, S. B., Connors, T. J., Weisberg, S. P., Krupska, I., Matsumoto, R., Poon, M. M. L., Idzikowski, E., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.005 · Cited by 378
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“Finally, we visualize the results with the UMAP algorithm [45].”
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“Seurat’s AddModuleScore function (control parameter = 8) was used to calculate gene module scores for all cells.”
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Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276
“We present this information in a two-dimensional plot of the annotated latent space (Figure 3C, Supplementary Figure 8), as embedded by scVI [29], and laid out by UMAP [41].”
MethodThe CRUK IMAXT Grand Challenge Team, O’Flanagan, C. H., Campbell, K. R., Zhang, A. W., Kabeer, F., Lim, J. L., Biele, J., Eirew, P. D., Lai, D., McPherson, A., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1830-0 · Cited by 265
Novel antigen-presenting cell imparts Treg-dependent tolerance to gut microbiota
Akagbosu, B., Tayyebi, Z., Shibu, G., Paucar Iza, Y. A., Deep, D., Parisotto, Y. F., Fisher, L., Pasolli, H. A., Thevin, V., Elmentaite, R., et al. (2022) · Nature · doi:10.1038/s41586-022-05309-5 · Cited by 273
GANs for Medical Image Synthesis: An Empirical Study
Skandarani, Y., Jodoin, P., & Lalande, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9030069 · Cited by 354
A sequence-based global map of regulatory activity for deciphering human genetics
Chen, K. M., Wong, A. K., Troyanskaya, O. G., & Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01102-2 · Cited by 269
Wang, R., Dang, M., Harada, K., Han, G., Wang, F., Pizzi, M. P., Zhao, M., Tatlonghari, G., Zhang, S., Hao, D., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-1125-8 · Cited by 278
Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 237
Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., Weiss, M. D., Li, X.-J., Savage, A. K., Green, R. R., et al. (2021) · eLife · doi:10.7554/elife.63632 · Cited by 267
“For display, we further reduced the dimensionality of selected PCs using UMAP (Becht et al., 2019; McInnes et al., 2018) (R package uwot, v0.”
MethodCumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
Li, B., Gould, J., Yang, Y., Sarkizova, S., Tabaka, M., Ashenberg, O., Rosen, Y., Slyper, M., Kowalczyk, M. S., Villani, A., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0905-x · Cited by 243
ShinyCell: simple and sharable visualization of single-cell gene expression data
Ouyang, J. F., Kamaraj, U. S., Cao, E. Y., & Rackham, O. J. L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab209 · Cited by 245
Deep learning as a tool for ecology and evolution
Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A. J., Valentini, G., & White, A. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13901 · Cited by 295
Breast tumours maintain a reservoir of subclonal diversity during expansion
Minussi, D. C., Nicholson, M. D., Ye, H., Davis, A., Wang, K., Baker, T., Tarabichi, M., Sei, E., Du, H., Rabbani, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03357-x · Cited by 256
Single-cell analysis of olfactory neurogenesis and differentiation in adult humans
Durante, M. A., Kurtenbach, S., Sargi, Z. B., Harbour, J. W., Choi, R., Kurtenbach, S., Goss, G. M., Matsunami, H., & Goldstein, B. J. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0587-9 · Cited by 254
Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview
Slovin, S., Carissimo, A., Panariello, F., Grimaldi, A., Bouchè, V., Gambardella, G., & Cacchiarelli, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1307-8_19 · Cited by 264
Single-cell atlas of colonic CD8+ T cells in ulcerative colitis
Corridoni, D., Antanaviciute, A., Gupta, T., Fawkner-Corbett, D., Aulicino, A., Jagielowicz, M., Parikh, K., Repapi, E., Taylor, S. L., Ishikawa, D., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1003-4 · Cited by 257
Machine Learning for Microcontroller-Class Hardware: A Review
Saha, S. S., Sandha, S. S., & Srivastava, M. B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210773 · Cited by 300
Chemotherapy Induces Senescence-Like Resilient Cells Capable of Initiating AML Recurrence
Duy, C., Li, M., Teater, M., Meydan, C., Garrett-Bakelman, F. E., Lee, T. C., Chin, C. R., Durmaz, C., Kawabata, K. C., Dhimolea, E., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1375 · Cited by 253
“F, UMAP plot showing the Research. on December 9, 2021.”
MethodInfluentialSTING inhibits the reactivation of dormant metastasis in lung adenocarcinoma
Hu, J. Y., Rivera, F. J. S., Wang, Z., Johnson, G. N., Ho, Y.-J., Ganesh, K., Umeda, S., Gan, S., Mujal, A. M., Delconte, R. B., et al. (2023) · Nature · doi:10.1038/s41586-023-05880-5 · Cited by 261
The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape
de Jong, M. M. E., Kellermayer, Z., Papazian, N., Tahri, S., Hofste op Bruinink, D., Hoogenboezem, R., Sanders, M. A., van de Woestijne, P. C., Bos, P. K., Khandanpour, C., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00931-3 · Cited by 250
Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio
Bala, P., Eisenreich, B. R., Yoo, S. B. M., Hayden, B. Y., Park, H. S., & Zimmermann, J. (2020) · Nature Communications · doi:10.1038/s41467-020-18441-5 · Cited by 238
Zhang, F., Mears, J., Shakib, L., Beynor, J. I., Shanaj, S., Korsunsky, I., Nathan, A., Donlin, L. T., & Raychaudhuri, S. (2021) · Genome Medicine · doi:10.1186/s13073-021-00881-3 · Cited by 251
Dress, R. J., Dutertre, C., Giladi, A., Schlitzer, A., Low, I., Shadan, N. B., Tay, A., Lum, J., Kairi, M. F. B. M., Hwang, Y. Y., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0420-3 · Cited by 248
Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
Liu, W., Bao, Q., Sun, Y. M., & Mei, T. (2022) · ACM Computing Surveys · doi:10.1145/3524497 · Cited by 270
“For visualization, UMAP [128] is employed to reduce the dimension.”
MethodDimensionality reduction by UMAP to visualize physical and genetic interactions
Dorrity, M. W., Saunders, L. M., Queitsch, C., Fields, S., & Trapnell, C. (2020) · Nature Communications · doi:10.1038/s41467-020-15351-4 · Cited by 255
Interpretable tree-based ensemble model for predicting beach water quality
Li, L., Qiao, J., Yu, G., Wang, L., Li, H.-Y., Liao, C., & Zhu, Z. (2022) · Water Research · doi:10.1016/j.watres.2022.118078 · Cited by 273
Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-51935-3_34 · Cited by 278
Metadata
- Authors
- McInnes, L., Healy, J., Saul, N., & Großberger, L.
- Affiliations
- Washington State University (US)
- Max Planck Society (DE)
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2018-09-02
- Volume
- 3
- Issue
- 29
- Pages
- 861
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W2889326414
- Cited by
- 9,826
- References
- 2
- Citations by year
- 2026: 1775 · 2025: 1933 · 2024: 1675 · 2023: 1392 · 2022: 1193 · 2021: 1058 · 2020: 587 · 2019: 186 · 2018: 24
- FWCI
- 232.44
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Journal of Open Source Software (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- UMAP, dimensionality reduction, manifold learning, nonlinear dimensionality reduction, data visualization, scikit-learn
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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