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 or uniform manifold approximation and projection, aim to preserve apparent pairwise distances approximately in a reduced two-dimensional space so that it can be readily visualized.
“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]
UMAP is a successor of t-SNE that is superior in run time performance, scalability for larger datasets, and preservation of data structure.
“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]
UMAP lacks maturity.
“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]
UMAP has improved time performance and better perseverance of data structure.
“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]
Cites
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 · see citing works
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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“Finally, we reduced the dimension of the input gate embedding layer (from R 256 to R ) so as to be able to visualize dominant features in the input embedding To do this we use a dimension reduction algorithm, called UMAP (McInnes et al., 2018) for "Uniform Manifold Approximation and Projection for Dimension Reduction".”
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“…intensity across all markers per cell, scaled (z-scores), and used to compute a two dimensional UniformManifold Approximation and Projection (McInnes et al., 2018) embedding using the R package uwot (n,neighbors = 50, n_epochs = 500, n_trees = 100, init = ‘‘pca,’’ min.dist = 0.1, metric =…”
InfluentialRosen, A., Iyer, S. M., Ray, D., Yao, Z., Aspuru‐Guzik, A., Gagliardi, L., Notestein, J. M., & Snurr, R. Q. (2021) · Matter · doi:10.1016/j.matt.2021.02.015 · Cited by 546 · see citing works
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
Notch signalling drives synovial fibroblast identity and arthritis pathology
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Ishigaki, K., Akiyama, M., Kanai, M., Takahashi, A., Kawakami, E., Sugishita, H., Sakaue, S., Matoba, N., Low, S.-K., Okada, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0640-3 · Cited by 508 · see citing works
Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus
Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., Sun, Y., Ogorodnikov, A., Bueno, R., Lu, A. C., et al. (2022) · Science · doi:10.1126/science.abf1970 · Cited by 507 · see citing works
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
Wang, A., Murdock, R., Kauwe, S. K., Oliynyk, A. O., Gurlo, A., Brgoch, J., Persson, K. A., & Sparks, T. D. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c01907 · Cited by 514 · see citing works
A noninflammatory mRNA vaccine for treatment of experimental autoimmune encephalomyelitis
Krienke, C., Kolb, L., Diken, E., Streuber, M., Kirchhoff, S., Bukur, T., Akilli-Öztürk, Ö., Kranz, L. M., Berger, H., Petschenka, J., et al. (2021) · Science · doi:10.1126/science.aay3638 · Cited by 520 · see citing works
Single-cell mutation analysis of clonal evolution in myeloid malignancies
Miles, L. A., Bowman, R. L., Merlinsky, T. R., Csete, I. S., Ooi, A. T., Durruthy-Durruthy, R., Bowman, M., Famulare, C. A., Patel, M. A., Méndez, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2864-x · Cited by 506 · see citing works
The emergence of transcriptional identity in somatosensory neurons
Sharma, N., Flaherty, K., Lezgiyeva, K., Wagner, D. E., Klein, A. M., & Ginty, D. D. (2020) · Nature · doi:10.1038/s41586-019-1900-1 · Cited by 498 · see citing works
Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works
Integrating spatial gene expression and breast tumour morphology via deep learning
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Blastocyst-like structures generated from human pluripotent stem cells
Yu, L., Wei, Y., Duan, J., Schmitz, D. A., Sakurai, M., Wang, L., Wang, K., Zhao, S., Hon, G. C., & Wu, J. (2021) · Nature · doi:10.1038/s41586-021-03356-y · Cited by 495 · see citing works
Sarkizova, S., Klaeger, S., Le, P. M., Li, L. W., Oliveira, G., Keshishian, H., Hartigan, C. R., Zhang, W., Braun, D. A., Ligon, K. L., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0322-9 · Cited by 505 · see citing works
Toroidal topology of population activity in grid cells
Gardner, R. J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N. A., Dunn, B. A., Moser, M.-B., & Moser, E. I. (2022) · Nature · doi:10.1038/s41586-021-04268-7 · Cited by 428 · see citing works
SNT: a unifying toolbox for quantification of neuronal anatomy
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Single-cell transcriptomic characterization of a gastrulating human embryo
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Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
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PLSDB: a resource of complete bacterial plasmids
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“Then, UMAP was applied to the distance matrix using parameters n neighbors=50, n components=2, init='random', metric='precomputed'.”
MethodInfluentialSingle-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages
Ochocka, N., Segit, P., Walentynowicz, K. A., Wojnicki, K., Cyranowski, S., Swatler, J., Mieczkowski, J., & Kaminska, B. (2021) · Nature Communications · doi:10.1038/s41467-021-21407-w · Cited by 466 · see citing works
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Qi, R., Wang, C., Fu, H., Ma, Q., & Xu, D. H. (2021) · Nature Communications · doi:10.1038/s41467-021-22197-x · Cited by 454 · see citing works
Artificial intelligence for natural product drug discovery
Mullowney, M. W., Duncan, K., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00774-7 · Cited by 507 · see citing works
“A recent development to t -SNE is the uniform manifold approximation and projection (UMAP) algorithm, which is less computationally expensive than the previous approach and can therefore be applied to larger datasets 227 .”
MethodMorphological diversity of single neurons in molecularly defined cell types
Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03941-1 · Cited by 417 · see citing works
Mapping transcriptomic vector fields of single cells
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“This set of genes was then used for PCA denoising on the size factor–normalized and log1p-transformed data of new RNA counts, followed by UMAP projection (McInnes et al., 2018).”
Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Ehinger, A., Wu, S. Z., Al-Eryani, G., Roden, D., Swarbrick, A., Borg, Å., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26271-2 · Cited by 404 · see citing works
Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06728-8 · Cited by 444 · see citing works
“13, we present samples from Chroma and a set of native structures with global topology descriptors derived from knot theory 43,44 and embed them into two dimensions with UMAP 45 .”
BackgroundMethodMassively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401 · see citing works
Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Reeves, J. W., Mohan, R., Drokhlyansky, E., Van Wittenberghe, N., Ashenberg, O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01134-8 · Cited by 426 · see citing works
Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., & Halperin, E. (2020) · Nature Communications · doi:10.1038/s41467-020-15816-6 · Cited by 427 · see citing works
Chan, J. M., Quintanal-Villalonga, À., Gao, V. R., Xie, Y., Allaj, V., Chaudhary, O., Masilionis, I., Egger, J. V., Chow, A. T., Walle, T., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.008 · Cited by 440 · see citing works
- 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 · 2012: 2
- 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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