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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]
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“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]
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“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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Sengupta, S., Das, S., Crespo, Â. C., Cornel, A. M., Patel, A. G., Mahadevan, N. R., Campisi, M., Ali, A. K., Sharma, B., Rowe, J. H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00427-5 · Cited by 101
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Wang, X., Zhou, R., Xiong, Y., Zhou, L., Yan, X., Wang, M., Fan, L., Xie, C., Zhang, Y., Huang, Z., et al. (2021) · Cell Research · doi:10.1038/s41422-020-00451-z · Cited by 99
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(2024) · doi:10.1038/s42256-024-00847-1 · Cited by 108
“…Table 6 for exact P values). g , Visualization of differentiation between agonist, antagonist and non-binding pairs in the protein–ligand functional effect test set using uniform manifold approximation and projection (UMAP) 74 to project PSICHIC fingerprints on a 2D plane (Supplementary Method 14).”
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“To project Raman profiles to a scRNA-seq embedding, we calculated a k -nearest neighbor ( k -NN) graph ( k = 15) on the scRNA-seq top 50 principal components with the cosine metric, and UMAP with the scanpy.tl.umap function in Scanpy 46 version 1.7.2 with default parameters.”
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“10 (77) on the same SNN graph used for clustering.”
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“In order to visualize how well the different classes are separated in the feature space, we extracted the 2208-dimensional feature vectors before the last fully-connected layer of DenseNet-161 and utilized Uniform Manifold Approximation and Projection [42] (UMAP) for dimension reduction.”
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“Validating these assignments, we visualized t-type labels from Patch-seq and reference datasets in a joint UMAP projection by using alignment methods from the Seurat package ( 33 ) and found strong correspondence (fig.”
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Woo, T., Liang, X., Evans, D. A., Fernandez, O., Kretschmer, F., Reiter, S., & Laurent, G. (2023) · Nature · doi:10.1038/s41586-023-06259-2 · Cited by 95
Venous-plexus-associated lymphoid hubs support meningeal humoral immunity
Fitzpatrick, Z., Zanluqui, N. G., Rosenblum, J. S., Tuong, Z. K., Lee, C. Y. C., Chandrashekhar, V., Negro‐Demontel, M. L., Stewart, A. P., Posner, D. A., Buckley, M. W., et al. (2024) · Nature · doi:10.1038/s41586-024-07202-9 · Cited by 98
Lin, Y., Ma, J., Sun, D., et al. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.139078 · Cited by 88
Continent-wide genomic signatures of adaptation to urbanisation in a songbird across Europe
Salmón, P., Jacobs, A., Ahrén, D., Biard, C., Dingemanse, N. J., Dominoni, D. M., Helm, B., Lundberg, M., Señar, J. C., Sprau, P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23027-w · Cited by 90
Enhance explainability of manifold learning
Han, H., Li, W., Wang, J., Qin, G., & Qin, X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.119 · Cited by 83
Choudhury, S., Moret, M., Salvy, P., Weilandt, D., Vassily Hatzimanikatis, A., & Mišković, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00519-y · Cited by 84
Kha, Q.-H., Ho, Q., & Le, N. Q. K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01034 · Cited by 87
Dykema, A. G., Zhang, J., Cheung, L. S., Connor, S. C., Zhang, B., Zeng, Z., Cherry, C., Li, T., Caushi, J. X., Nishimoto, M., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.adg1487 · Cited by 88
AI‐organoid integrated systems for biomedical studies and applications
Maramraju, S., Kowalczewski, A. C., Kaza, A., Liu, X., Singaraju, J. P., Albert, M. V., Ma, Z., & Yang, H. (2024) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10641 · Cited by 95
“56 After data dimensionality reduction, clustering algorithms are required to identify clusters within the feature space.”
MethodTierney, B. T., Kim, J., Overbey, E. G., Ryon, K. A., Foox, J., Sierra, M. A., Bhattacharya, C., Damle, N., Najjar, D., Park, J., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01635-8 · Cited by 89
Yeh, C. Y., Aguirre, K., Laveroni, O., Kim, S., Wang, A., Liang, B., Zhang, X., Han, L. M., Valbuena, R., Bassik, M. C., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01943-5 · Cited by 98
“P values denote per tissue core if the expression of the CD8 TIP is significantly higher in CD8 + T cells with a high (above median) versus low (below median) abundance of malignant cells within a radius of 30 μm (one-sided t -test). d , UMAP embedding of CD8 + T cells (Validation 1 dataset) from gene expression alone…”
InfluentialIdentification of proliferating neural progenitors in the adult human hippocampus
Dumitru, I., Paterlini, M., Zamboni, M., Ziegenhain, C., Giatrellis, S., Saghaleyni, R., Björklund, Å. K., Alkass, K., Tata, M., Druid, H., et al. (2025) · Science · doi:10.1126/science.adu9575 · Cited by 94
Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization
Fdez, J., Guttenberg, N., Witkowski, O., & Pasquali, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.626277 · Cited by 94
“Interestingly, the UMAP for batch normalization shows a handful of compact clusters.”
MethodInfluentialBach, K., Pensa, S., Zarocsinceva, M., Kania, K., Stockis, J., Pinaud, S., Lazarus, K. A., Shehata, M., Simões, B. M., Greenhalgh, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21783-3 · Cited by 86
Bayesian inference of gene expression states from single-cell RNA-seq data
Breda, J., Zavolan, M., & van Nimwegen, E. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00875-x · Cited by 92
A deep generative model enables automated structure elucidation of novel psychoactive substances
Skinnider, M. A., Wang, F., Pasin, D., Greiner, R., Foster, L. J., Dalsgaard, P. W., & Wishart, D. S. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00407-x · Cited by 96
Marshall, J. L., Noel, T., Wang, Q. S., Chen, H., Murray, E. M., Subramanian, A., Vernon, K. A., Bazúa‐Valenti, S., Liguori, K., Keller, K. H., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104097 · Cited by 87
“For dimensionality reduction, we performed uniform manifold approximation and reduction (UMAP) with the Python package umap-learn (v. 0.4.6) (McInnes et al., 2018).”
InfluentialHolm, J. S., Funt, S. A., Borch, A., Munk, K. K., Bjerregaard, A.-M., Reading, J. L., Maher, C. A., Regazzi, A. M., Wong, P., Al‐Ahmadie, H. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29342-0 · Cited by 89
Eye Know You Too: Toward Viable End-to-End Eye Movement Biometrics for User Authentication
Lohr, D. J., & Komogortsev, O. V. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3201369 · Cited by 97
Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
Li, H., Cui, J. H., Zhang, X., Han, Y., & Cao, L. (2022) · Remote Sensing · doi:10.3390/rs14184579 · Cited by 87
Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Gao, K., Wang, R., Chen, J., Cheng, L., Frishcosy, J., Huzumi, Y., Qiu, Y., Schluckbier, T., Wei, X., & Wei, G. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00965 · Cited by 93
Innate cell profiles during the acute and convalescent phase of SARS-CoV-2 infection in children
Neeland, M. R., Bannister, S., Clifford, V., Dohle, K., Mulholland, K., SUTTON, P. R. N., Curtis, N. C., Steer, A. C., Burgner, D. P., Crawford, N. W., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21414-x · Cited by 84
Pawlowicz, L. M., & Downum, C. E. (2021) · Journal of Archaeological Science · doi:10.1016/j.jas.2021.105375 · Cited by 86
A transcriptional rheostat couples past activity to future sensory responses
Tsukahara, T., Brann, D. H., Pashkovski, S. L., Guitchounts, G., Bozza, T. C., & Datta, S. R. (2021) · Cell · doi:10.1016/j.cell.2021.11.022 · Cited by 77
“UMAP was used solely for visualization purposes (McInnes et al., 2018).”
Method
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
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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.
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