Cited by 9,826Face and Expression RecognitionTopological and Geometric Data AnalysisStochastic Gradient Optimization Techniques

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UMAP: Uniform Manifold Approximation and Projection

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McInnes, Leland, Healy, John, Saul, Nathaniel, Großberger, Lukas·2018·Journal of Open Source Software

DOI 10.21105/joss.00861

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Abstract

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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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
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McInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22
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Silcocks, M., Farlow, A., Hermes, A., Tsambos, G., Patel, H. R., Huebner, S., Baynam, G. S., Jenkins, M. R., Vukcevic, D., Easteal, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06831-w · Cited by 75
“UMAP (v.0.2.7.0) 70 was applied as per ref.”
MethodInfluential
Neural signatures of natural behaviour in socializing macaques
Testard, C., Tremblay, S., Parodi, F., DiTullio, R. W., Acevedo-Ithier, A., Gardiner, K. L., Kording, K. P., & Platt, M. L. (2024) · Nature · doi:10.1038/s41586-024-07178-6 · Cited by 85
BBrowser: Making single-cell data easily accessiblePreprint
Le, T., Phan, T., Pham, M., Tran, D., Lam, L., Nguyen, N. T., Truong, T., Vuong, H., Luu, T., Phung, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.11.414136 · Cited by 77
A single cell transcriptome atlas of the developing zebrafish hindbrain
Tambalo, M., Mitter, R., & Wilkinson, D. G. (2020) · Development · doi:10.1242/dev.184143 · Cited by 74
“…cord have been defined as non-apical progenitors, able to generate one excitatory (V2a) and one inhibitory (V2b) interneuron, and have been proposed to be a pool important for rapid generation of the sensory-locomotor circuit (McIntosh et al., 2017); their molecular signature is reported in Fig.”
Background
Evolution of metabolic capabilities and molecular features of diplonemids, kinetoplastids, and euglenids
Butenko, A., Opperdoes, F. R., Flegontova, O., Horák, A., Hampl, V., Keeling, P. J., Gawryluk, R. M. R., Tikhonenkov, D. V., Flegontov, P., & Lukeš, J. (2020) · BMC Biology · doi:10.1186/s12915-020-0754-1 · Cited by 72
“(UMAP) is a novel general-purpose non-linear algorithm for dimensionality reduction [60].”
BackgroundMethod
Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach
Pereira, I. G., Guerin, J. M., Silva, A., Garcia, G. S., Piscitelli, P., Miani, A., Distante, C., & Gonçalves, L. M. G. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17145115 · Cited by 74
Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer
Tercan, H., Deibert, P., & Meisen, T. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01793-0 · Cited by 74
“To test this, we extract the trained weights (including the bias weight) of all output heads after a fully trained sequence of 16 tasks and visualize them in a two-dimensional space using the dimensionality reduction method UMAP (McInnes et al. 2018), see Fig.”
Method
Plasmacytoid dendritic cell activation is dependent on coordinated expression of distinct amino acid transporters
Grzes, K. M., Sanin, D. E., Kabat, A. M., Stanczak, M. A., Edwards-Hicks, J., Matsushita, M., Hackl, A., Hässler, F., Knoke, K., Zahalka, S., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.10.009 · Cited by 70
Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level
Qi, R., & Zou, Q. (2023) · Research · doi:10.34133/research.0050 · Cited by 74
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
Huang, X. Y., Ma, S., Zhao, C., Wang, H., & Ju, S. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01154-w · Cited by 81
Custom CRISPR–Cas9 PAM variants via scalable engineering and machine learning
Silverstein, R. A., Kim, N., Kroell, A.-S. H., Walton, R. T., Delano, J., Butcher, R. M., Pačesa, M., Smith, B., Christie, K. A., Ha, L. L., et al. (2025) · Nature · doi:10.1038/s41586-025-09021-y · Cited by 87
A single-parasite transcriptional atlas of Toxoplasma Gondii reveals novel control of antigen expression
Xue, Y., Theisen, T. C., Rastogi, S., Ferrel, A., Quake, S. R., & Boothroyd, J. C. (2020) · eLife · doi:10.7554/elife.54129 · Cited by 74
“To identify transcriptomic changes associated with the tachyzoite-bradyzoite transition, we next projected data from both induced and uninduced Pru parasites onto two dimensions using UMAP, a nonlinear dimensionality reduction method (Materials and methods) (McInnes et al., 2018).”
Method
Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings
Dai, Y., Guo, C., Guo, W., & Eickhoff, C. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa256 · Cited by 77
Innate immune evasion revealed in a colorectal zebrafish xenograft model
Póvoa, V., de Almeida, C. R., Maia-Gil, M., Sobral, D., Domingues, M., Martínez-López, M. F., Fuzeta, M. D. A., Silva, C., Grosso, A. R., & Fior, R. (2021) · Nature Communications · doi:10.1038/s41467-021-21421-y · Cited by 72
Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN
Kinman, L. F., Powell, B. M., Zhong, E. D., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00763-x · Cited by 72
“…analyze command automates these tasks by: 1) performing principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP; McInnes et al., 2018) on the latent embeddings to aid in visualization of high (>2) dimensional latent spaces (see Glossary); 2) generating…”
Method
Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing
Garcia‐Flores, V., Romero, R. J., Tarca, A. L., Peyvandipour, A., Xu, Y., Galaz, J., Miller, D., Chaiworapongsa, T., Chaemsaithong, P., Berry, S. M., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adh8335 · Cited by 76
Towards retraining-free RNA modification prediction with incremental learning
Qiao, J., Jin, J., Yu, H., & Wei, L. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120105 · Cited by 70
Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling
Chierici, M., Bussola, N., Marcolini, A., Francescatto, M., Zandonà, A., Trastulla, L., Agostinelli, C., Jurman, G., & Furlanello, C. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01065 · Cited by 82
“Unsupervised Analysis The features selected by juXT, rSNF and rSNFi are projected on a bi-dimensional space using the UMAP unsupervised multidimensional projection method (42, 43).”
Method
The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions
Hargreaves, C. J., Dyer, M. S., Gaultois, M. W., Kurlin, V. A., & Rosseinsky, M. J. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c03381 · Cited by 66
Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H 2 O 2 Synthesis through Active Motif Screening
Back, S., Na, J., & Ulissi, Z. W. (2021) · ACS Catalysis · doi:10.1021/acscatal.0c05494 · Cited by 69
Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses
Zheng, H., Rao, A., Đermadi, D., Toh, J., Jones, L. M., Donato, M., Liu, Y. E., Su, Y., Dai, C. L., Kornilov, S. A., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.002 · Cited by 69
Identifying Market Structure: A Deep Network Representation Learning of Social Engagement
Yang, Y., Zhang, K., & Kannan, P. K. (2021) · Journal of Marketing · doi:10.1177/00222429211033585 · Cited by 73
AI in drug development: a multidisciplinary perspective
Gallego, V., Naveiro, R., Roca, C., Insua, D. R., & Campillo, N. E. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10266-8 · Cited by 85
IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism
Laghrissi, F., Douzi, S., Douzi, K., & Hssina, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00544-5 · Cited by 78
Automated real estate valuation with machine learning models using property descriptions
Baur, K., Rosenfelder, M., & Lutz, B. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119147 · Cited by 91
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Vahidian, S., Morafah, M., Chen, C., Shah, M., & Lin, B. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3293068 · Cited by 82
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Shimakawa, H., Kumada, A., & Sato, M. (2024) · npj Computational Materials · doi:10.1038/s41524-023-01194-2 · Cited by 81

Authors: McInnes, L., Healy, J., Saul, N., & Großberger, L.
Affiliations: Washington State University (US)
Max Planck Society (DE)
ORCID: McInnes, Leland
Healy, John
Saul, Nathaniel
Großberger, Lukas
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)
DOI: 10.21105/joss.00861
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
creativecommons.org/licenses/by/4.0/ (am)
creativecommons.org/licenses/by/4.0/ (tdm)
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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UMAP: Uniform Manifold Approximation and Projection

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