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

The DOI matches its Crossref record; 1 of the 2 references it cites were found in a registry or an index.

UMAP: Uniform Manifold Approximation and Projection

Open access

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

DOI 10.21105/joss.00861

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.21105/joss.00861

Cites

1 of 2 references verified0 citation errors · 1 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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-citation
McInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22
(n.d.)
Unresolved

Flagged first, then by citations

Show all 2 references

1 of the 2 references couldn’t be matched to a known paper.

Sorted by citations

Items 501–600 of 1,065

Single-cell analysis of human MAIT cell transcriptional, functional and clonal diversity
Garner, L. C., Amini, A., FitzPatrick, M. E. B., Lett, M. J., Hess, G. F., Sinnreich, M. F., Provine, N. M., & Klenerman, P. (2023) · Nature Immunology · doi:10.1038/s41590-023-01575-1 · Cited by 120
Context-Specific Transcription Factor Functions Regulate Epigenomic and Transcriptional Dynamics during Cardiac Reprogramming
Stone, N. R., Gifford, C. A., Thomas, R., Pratt, K. J. B., Samse, K. M., Mohamed, T., Radzinsky, E., Schricker, A., Ye, L., Yu, P., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.06.012 · Cited by 115
Single-cell transcriptomics of the naked mole-rat reveals unexpected features of mammalian immunity
Hilton, H. G., Rubinstein, N. D., Janki, P., Ireland, A. T., Bernstein, N., Fong, N. L., Wright, K. M., Smith, M., Finkle, D., Martin-McNulty, B., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000528 · Cited by 119
“Figure 1: Single-cell transcriptional profiling of mouse and naked mole-rat spleens reveals major differences in immune cell populations a - Schematic showing the workflow in which single-cell suspensions are derived from the spleens of four (two males [M], two females [F]) C57BL/6 mice (upper panel, grey) or four nak…”
BackgroundInfluential
Sequence-to-function deep learning frameworks for engineered riboregulators
Valeri, J. A., Collins, K. M., Ramesh, P., Alcantar, M. A., Lepe, B. A., Lu, T. K., & Camacho, D. M. (2020) · Nature Communications · doi:10.1038/s41467-020-18676-2 · Cited by 124
Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer
Zhang, L., Zhang, Y., Wang, C., Yang, Y., Ni, Y., Wang, Z., Song, T., Yao, M., Liu, Z., Chao, N., et al. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00824-9 · Cited by 121
Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST
Liu, W., Liao, X., Luo, Z., Yang, Y., Lau, M. C., Jiao, Y., Shi, X., Zhai, W., Ji, H., Yeong, J. P.-S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35947-w · Cited by 121
“First, users can visualize the inferred embeddings for biological effects between cell/domain types using two components from either tSNE 31 or UMAP 32 .”
Single-cell transcriptomic atlas-guided development of CAR-T cells for the treatment of acute myeloid leukemia
Gottschlich, A., Thomas, M., Grünmeier, R., Lesch, S., Rohrbacher, L., Igl, V., Briukhovetska, D., Benmebarek, M.-R., Vick, B., Dede, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01684-0 · Cited by 123
Deep learning and the global workspace theory
VanRullen, R., & Kanai, R. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.005 · Cited by 128
“…translation in deep learning [22–33], here we emphasize cycle consistency as the major unsupervised training objective for neural translation (see Regularly sampling this space in a 2D matrix produces the image reconstructions on the right (created using the UMAP inverse transform [17]).”
Method
Distinct regional ontogeny and activation of tumor associated macrophages in human glioblastoma
Landry, A., Balas, M., Alli, S. B., Spears, J., & Zador, Z. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-76657-3 · Cited by 114
Conserved and divergent gene regulatory programs of the mammalian neocortex
Zemke, N. R., Armand, E. J., Wang, W., Lee, S. Y., Zhou, J., Li, Y. E., Liu, H., Tian, W., Nery, J. R., Castanon, R., et al. (2023) · Nature · doi:10.1038/s41586-023-06819-6 · Cited by 117
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
Hyun, J., Kim, S., Jeon, G., Kim, S. H., Bae, K., & Kang, B. J. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00205 · Cited by 140
Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communitiesPreprint
Beaulaurier, J., Luo, E., Eppley, J. M., Uyl, P. A. D., Dai, X., Burger, A. H., Turner, D., Pendelton, M., Juul, S., Harrington, E. D., et al. (2020) · Genome Research · doi:10.1101/gr.251686.119 · Cited by 114
“3.2 (McInnes et al. 2018) using the options n_neighbors = 15 and min_dist = 0.1.”
Influential
A demonstration of unsupervised machine learning in species delimitation
Derkarabetian, S., Castillo, S., Koo, P. K., Ovchinnikov, S., & Hedin, M. C. (2019) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2019.106562 · Cited by 109
“An alternative to t-SNE, the recently developed UMAP (McInnes et al. 2018; uniform manifold approximation and projection) produces clusters which are shown to have biological meaning (Becht et al. 2018).”
Method
Single cell transcriptomics identifies stem cell-derived graft composition in a model of Parkinson’s disease
Tiklová, K., Nolbrant, S., Fiorenzano, A., Björklund, Å. K., Sharma, Y., Heuer, A., Gillberg, L., Hoban, D. B., Cardoso, T., Adler, A. F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16225-5 · Cited by 125
Artificial Intelligence in Advertising Creativity
Vakratsas, D., & Wang, X. (. (2020) · Journal of Advertising · doi:10.1080/00913367.2020.1843090 · Cited by 114
CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information
Wang, S., Herriges, M. J., Hurley, K., Kotton, D. N., & Klein, A. M. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01209-1 · Cited by 112
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Ermolov, A., Mirvakhabova, L., Khrulkov, V., Sebe, N., & Oseledets, I. V. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00726 · Cited by 139
“DINO [3] is a recently proposed method, where vision transformer is trained in the self-supervised learning setting.”
BackgroundMethodInfluential
Identification of ALK in thinness
Orthofer, M., Valsesia, A., Mägi, R., Wang, Q.-P., Kaczanowska, J., Kozieradzki, I., Leopoldi, A., Cikes, D., Zopf, L. M., Tretiakov, E. O., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.034 · Cited by 117
“…for Sst neurons (Romanov et al., 2017)): five cell groups (Figure 5C) were obtained and processed by the SCT-pipeline of Seurat 3.1 package (Hafemeister and Satija, 2019; Stuart et al., 2019) and visualized by uniform manifold approximation and projection (UMAP) algorithm (McInnes et al., 2018).”
BackgroundMethodInfluential
Predict Alzheimer’s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations
Katabathula, S., Wang, Q., & Xu, R. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00837-0 · Cited by 116
Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges
Achaal, B., Adda, M., Berger, M., Ibrahim, H., & Awde, A. (2024) · Cybersecurity · doi:10.1186/s42400-023-00200-w · Cited by 127
“The UMAP is a high-performance dimension reduction algorithm proposed in McInnes et al. (2018).”
Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
Sakaue, S., Hirata, J., Kanai, M., Ken, S., Akiyama, M., Too, C. L., Arayssi, T., Hammoudeh, M., Emadi, S. A., Masri, B. K., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15194-z · Cited by 111
Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing
Allen, W. E., Altae-Tran, H., Briggs, J., Jin, X., McGee, G., Shi, A., Raghavan, R. S., Kamariza, M., Nova, N., Pereta, A., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00944-2 · Cited by 106
DNA Methylation Profiling of Human Hepatocarcinogenesis
Hernandez‐Meza, G., von Felden, J., Gonzalez‐Kozlova, E., García‐Lezana, T., Peix, J., Portela, A., Craig, A. J., Sayols, S., Schwartz, M., Losic, B., et al. (2020) · Hepatology · doi:10.1002/hep.31659 · Cited by 116
Diversity amongst human cortical pyramidal neurons revealed via their sag currents and frequency preferences
Chameh, H. M., Rich, S., Wang, L., Chen, F., Zhang, L., Carlen, P. L., Tripathy, S. J., & Valiante, T. A. (2021) · Nature Communications · doi:10.1038/s41467-021-22741-9 · Cited by 110
Adaptation to chronic ER stress enforces pancreatic β-cell plasticity
Chen, C.-W., Guan, B.-J., Alzahrani, M. R., Gao, Z., Gao, L., Bracey, S., Wu, J., Mbow, C. A., Jobava, R., Haataja, L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32425-7 · Cited by 116
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Ding, J., & Regev, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22851-4 · Cited by 110
“Embedding of stromal cells and glia cells on a sphere, with patient origin of cells as the batch vector (A), by UMAP embedding with 20 batch-corrected principal components from Harmony as inputs (B), or on a sphere when both the patient and disease status were used as the batch vector (C).”
BackgroundInfluential
scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
Sun, T., Song, D., Li, W. V., & Li, J. J. (2021) · Genome biology · doi:10.1186/s13059-021-02367-2 · Cited by 115
Review of single-cell RNA-seq data clustering for cell-type identification and characterization
Zhang, S., Li, X., Lin, J., Lin, Q., & Wong, K. (2023) · RNA · doi:10.1261/rna.078965.121 · Cited by 120
Individual cell types in C. elegans age differently and activate distinct cell-protective responses
Roux, A. E., Yuan, H., Podshivalova, K., Hendrickson, D. G., Kerr, R. A., Kenyon, C. J., & Kelley, D. R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112902 · Cited by 116
ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer
El-Ghaish, H., & Eldele, E. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105714 · Cited by 111
PIEZO-dependent mechanosensing is essential for intestinal stem cell fate decision and maintenance
Baghdadi, M. B., Houtekamer, R. M., Perrin, L., Rao‐Bhatia, A., Whelen, M., Decker, L., Bergert, M., Gonzalez, C. P., Bouras, R., Gropplero, G., et al. (2024) · Science · doi:10.1126/science.adj7615 · Cited by 125
Myc and Dnmt1 impede the pluripotent to totipotent state transition in embryonic stem cells
Fu, X., Wu, X., Djekidel, M. N., & Zhang, Y. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0343-0 · Cited by 108
Objective assessment of stored blood quality by deep learning
Doan, M., Sebastian, J. A., Caicedo, J. C., Siegert, S., Roch, A., Turner, T. R., Mykhailova, O. O., Pinto, R. N., McQuin, C., Goodman, A., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001227117 · Cited by 108
SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks
Dibaeinia, P., & Sinha, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.003 · Cited by 120
“We used PCA to reduce the dimensionality of these data to the first 10 PCs, and then applied UMAP to obtain a two dimensional representation of single cells.”
Influential
OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data
Zhang, X., Xing, Y., Sun, K., & Guo, Y. (2021) · Cancers · doi:10.3390/cancers13123047 · Cited by 119
Learning the natural history of human disease with generative transformers
Shmatko, A., Jung, A. W., Gaurav, K., Brunak, S., Mortensen, L. H., Birney, E., Fitzgerald, T., & Gerstung, M. (2025) · Nature · doi:10.1038/s41586-025-09529-3 · Cited by 110
Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design
Lin, E., Lin, C., & Lane, H. (2020) · Molecules · doi:10.3390/molecules25143250 · Cited by 113
Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph
Zhou, J., Hu, C., Chi, J., Wu, J., Shen, M., & Xuan, Q. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3208471 · Cited by 113
Graded BMP signaling within intestinal crypt architecture directs self-organization of the Wnt-secreting stem cell niche
Kraiczy, J., McCarthy, N., Malagola, E., Tie, G., Madha, S., Boffelli, D., Wagner, D. E., Wang, T. C., & Shivdasani, R. A. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.03.004 · Cited by 112
Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer’s disease
Miyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115
De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization
Levitin, H. M., Yuan, J., Cheng, Y. L., Ruiz, F. J. R., Bush, E. C., Bruce, J. N., Canoll, P., Iavarone, A., Lasorella, A., Blei, D. M., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188557 · Cited by 98
“…use scHPF’s factorization as a low-dimensional input to t-distributed stochastic neighbor embedding (tSNE; Maaten & Hinton, 2008) or uniform manifold approximation and projection (UMAP; McInnes et al , 2018) to produce visualizations that were consistent with conventional clustering (Fig EV5).”
A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity
Zhang, W., Hawkins, P. G., He, J., Gupta, N. T., Liu, J., Choonoo, G., Jeong, S., Chen, C. R., Dhanik, A., Dillon, M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf5835 · Cited by 107
Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease
Nathan, A., Beynor, J. I., Baglaenko, Y., Suliman, S., Ishigaki, K., Asgari, S., Huang, C.-C., Luo, Y., Zhang, Z., Lopez, K., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00933-1 · Cited by 103
The developing mouse coronal suture at single-cell resolution
Farmer, D. T., Mlčochová, H., Zhou, Y., Koelling, N., Wang, G., Ashley, N., Bugacov, H., Chen, H.-J., Parvez, R. K., Tseng, K.-C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24917-9 · Cited by 107
Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data
Armstrong, G., Martino, C., Rahman, G., González, A., Vázquez‐Baeza, Y., Mishne, G., & Knight, R. D. (2021) · mSystems · doi:10.1128/msystems.00691-21 · Cited by 116
Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing
Liu, J., Tran, V., Vemuri, V. N. P., Byrne, A., Borja, M., Kim, Y. J., Agarwal, S., Wang, R., Awayan, K., Murti, A., et al. (2022) · Life Science Alliance · doi:10.26508/lsa.202201701 · Cited by 109
“The resulting neighborhood graph and UMAP plots were computed on the scVI latent space.”
Influential
scm6A-seq reveals single-cell landscapes of the dynamic m6A during oocyte maturation and early embryonic development
Yao, H., Gao, C., Zhang, D., Xu, J., Song, G., Fan, X., Liang, D.-B., Chen, Y., Li, Q., Guo, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35958-7 · Cited by 112
Explainable multi-task learning for multi-modality biological data analysis
Tang, X., Zhang, J., He, Y., Zhang, X., Lin, Z., Partarrieu, S., Hanna, E. B., Ren, Z., Shen, H., Yang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37477-x · Cited by 122
Lineage-specific intolerance to oncogenic drivers restricts histological transformation
Gardner, E. E., Earlie, E. M., Li, K., Thomas, J., Hubisz, M. J., Stein, B. D., Zhang, C., Cantley, L. C., Laughney, A. M., & VARMUS, H. E. (2024) · Science · doi:10.1126/science.adj1415 · Cited by 116
JAK-STAT signaling maintains homeostasis in T cells and macrophages
Fortelny, N., Farlik, M., Fife, V. E. R., Gorki, A.-D., Lassnig, C., Maurer, B., Meissl, K., Dolezal, M., Boccuni, L., Geetha, A. R. S. J., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01804-1 · Cited by 112
PGC1/PPAR drive cardiomyocyte maturation at single cell level via YAP1 and SF3B2
Murphy, S. A., Miyamoto, M., Kervadec, A., Kannan, S., Tampakakis, E., Kambhampati, S., Lin, B. L., Paek, S., Andersen, P., Lee, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21957-z · Cited by 107
AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
Aliee, H., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.006 · Cited by 103
Generalized and scalable trajectory inference in single-cell omics data with VIA
Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K., & Tsia, K. K. (2021) · Nature Communications · doi:10.1038/s41467-021-25773-3 · Cited by 109
ERK1/2 phosphorylation predicts survival following anti-PD-1 immunotherapy in recurrent glioblastoma
Arrieta, V. A., Chen, A., KANE, J. R., Kang, S. J., Kassab, C., Dmello, C., Zhao, J., Burdett, K. B., Upadhyayula, P. S., Lee-Chang, C., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00260-2 · Cited by 108
A text-embedding-based approach to measuring patent-to-patent technological similarity
Hain, D. S., Jurowetzki, R., Buchmann, T. O., & Wolf, P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121559 · Cited by 129
Distinct transcriptome architectures underlying lupus establishment and exacerbation
Nakano, M., Ota, M., Takeshima, Y., Iwasaki, Y., Hatano, H., Nagafuchi, Y., Itamiya, T., Maeda, J., Yoshida, R., Yamada, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.07.021 · Cited by 110
Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag
Meers, M. P., Llagas, G., Janssens, D. H., Codomo, C. A., & Henikoff, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01522-9 · Cited by 105
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease
Ameen, M., Sundaram, L., Shen, M., Banerjee, A., Kundu, S., Nair, S., Shcherbina, A., Gu, M., Wilson, K. D., Varadarajan, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.028 · Cited by 113
Global organelle profiling reveals subcellular localization and remodeling at proteome scale
Hein, M. Y., Peng, D., Todorova, V., McCarthy, F., Kim, K., Liu, C., Savy, L., Januel, C., Baltazar-Nunez, R., Sekhar, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.028 · Cited by 130
Operando probing dynamic migration of copper carbonyl during electrocatalytic CO2 reduction
Yang, Y., Feijóo, J., Figueras, M., Chen, C., Shi, C., Guzman, M. V. F., Maombi, Y. M., Liu, S., Jain, P., Martos, V. B., et al. (2025) · Nature Catalysis · doi:10.1038/s41929-025-01359-w · Cited by 128
Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation
Selewa, A., Dohn, R., Eckart, H., Lozano, S., Xie, B., Gauchat, E., Elorbany, R., Rhodes, K., Burnett, J., Gilad, Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58327-6 · Cited by 102
Activity-dependent modulation of synapse-regulating genes in astrocytes
Farhy-Tselnicker, I., Boisvert, M., Liu, H., Dowling, C. C., Erikson, G. A., Blanco‐Suárez, E., Farhy, C., Shokhirev, M. N., Ecker, J. R., & Allen, N. J. (2021) · eLife · doi:10.7554/elife.70514 · Cited by 108
Peak learning of mass spectrometry imaging data using artificial neural networks
Abdelmoula, W. M., Lopez, B. G. C., Randall, E. C., Kapur, T., Sarkaria, J. N., White, F. M., Agar, J. N., Wells, W. M., & Agar, N. Y. R. (2021) · Nature Communications · doi:10.1038/s41467-021-25744-8 · Cited by 108
Navigating artificial general intelligence development: societal, technological, ethical, and brain-inspired pathways
Raghu Raman, R., Kowalski, R. M., Achuthan, K., Iyer, A., & Nedungadi, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-92190-7 · Cited by 136
Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota
Rozera, T., Pasolli, E., Segata, N., & Ianiro, G. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.02.035 · Cited by 112
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
Minnich, A., McLoughlin, K., Tse, M. J., Deng, J., Weber, A. D., Murad, N., Madej, B. D., Ramsundar, B., Rush, T., Calad-Thomson, S., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01053 · Cited by 104
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges
Rahnenführer, J., Bin, R. D., Benner, A., Ambrogi, F., Lusa, L., Boulesteix, A., Migliavacca, E., Binder, H., Michiels, S., Sauerbrei, W., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02858-y · Cited by 132
Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin
Liang, W.-W., Lu, R. J.-H., Jayasinghe, R. G., Foltz, S. M., Porta-Pardo, E., Geffen, Y., Wendl, M. C., Lazcano, R., Kolodziejczak, I., Song, Y., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.07.013 · Cited by 105
Reconstructing growth and dynamic trajectories from single-cell transcriptomics data
Sha, Y., Qiu, Y., Zhou, P., & Nie, Q. H. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00763-w · Cited by 119
“UMAP and reversible UMAP used top 30 PCs. AE takes the log-transformed matrix without standardization, and details of its architecture and training procedure are discussed in Supplementary Note 1.”
Influential
Spatial mapping of transcriptomic plasticity in metastatic pancreatic cancer
Pei, G., Min, J., Rajapakshe, K., Branchi, V., Liu, Y., Selvanesan, B. C., Thege, F. I., Sadeghian, D., Zhang, D., Cho, K. S., et al. (2025) · Nature · doi:10.1038/s41586-025-08927-x · Cited by 121
“To spatially represent clonality, we used the Loupe Browser and the ‘SpatialDimPlot’ function of Seurat.”
Influential
Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis
Trzupek, D., Dunstan, M., Cutler, A. J., Lee, M., Godfrey, L., Jarvis, L., Rainbow, D. B., Aschenbrenner, D., Jones, J. L., Uhlig, H. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00756-z · Cited by 105
Experience-dependent contextual codes in the hippocampus
Plitt, M. H., & Giocomo, L. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00816-6 · Cited by 101
Melanoma detection using adversarial training and deep transfer learning
Zunair, H., & Hamza, A. B. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab86d3 · Cited by 104
Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
Cao, K., Hong, Y., & Wan, L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab594 · Cited by 106
SCS: cell segmentation for high-resolution spatial transcriptomics
Chen, H., Li, D., & Bar‐Joseph, Z. (2023) · Nature Methods · doi:10.1038/s41592-023-01939-3 · Cited by 102
De novo design of self-assembling peptides with antimicrobial activity guided by deep learning
Liu, H., Song, Z., Zhang, Y. L., Wu, B., Chen, D., Zhou, Z., Zhang, H., Li, S., Feng, X., Huang, J., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02164-3 · Cited by 133
Advances and opportunities in image analysis of bacterial cells and communities
Jeckel, H., & Drescher, K. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa062 · Cited by 106
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
Wen, M., Blau, S. M., Spotte‐Smith, E. W. C., Dwaraknath, S., & Persson, K. A. (2020) · Chemical Science · doi:10.1039/d0sc05251e · Cited by 108
Simultaneous deep generative modelling and clustering of single-cell genomic data
Liu, Q., Chen, S., Jiang, R., & Wong, W. H. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00333-y · Cited by 103
Epithelial cell plasticity drives endoderm formation during gastrulation
Scheibner, K., Schirge, S., Burtscher, I., Büttner, M., Sterr, M., Yang, D., Böttcher, A., Ansarullah, M., Irmler, M., Beckers, J., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00694-x · Cited by 92
Disrupting biological sensors of force promotes tissue regeneration in large organisms
Chen, K., Kwon, S. H., Henn, D., Kuehlmann, B. A., Tevlin, R., Bonham, C. A., Griffin, M., Trotsyuk, A. A., Borrelli, M. R., Noishiki, C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25410-z · Cited by 110
The purinergic P2Y14 receptor links hepatocyte death to hepatic stellate cell activation and fibrogenesis in the liver
Mederacke, I., Filliol, A., Affo, S., Nair, A., Hernandez, C., Sun, Q., Hamberger, F., Brundu, F., Chen, Y., Ravichandra, A., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abe5795 · Cited by 103
Explainable machine learning framework to predict personalized physiological aging
Bernard, D., Doumard, E., Ader, I., Kémoun, P., Pagès, J., Galinier, A., Cussat‐Blanc, S., Furger, F., Ferrucci, L., Aligon, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13872 · Cited by 108
“2.4.2 | Data visualization A projection was made using the Uniform Manifold Approximation and Projection (UMAP) algorithm (McInnes et al., 2018).”
MethodInfluential
Liver CPT1A gene therapy reduces diet‐induced hepatic steatosis in mice and highlights potential lipid biomarkers for human NAFLD
Weber, M., Mera, P., Casas, J., Salvador, J., Rodríguez, A., Alonso, S., Sebastián, D., Soler‐Vázquez, M. C., Montironi, C., Recalde, S., et al. (2020) · The FASEB Journal · doi:10.1096/fj.202000678r · Cited by 107
Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia
Kreutmair, S., Unger, S., Núñez, N. G., Ingelfinger, F., Alberti, C., Feo, D. D., Krishnarajah, S., Kauffmann, M., Friebel, E., Babaei, S., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.05.002 · Cited by 109
Single-cell profiling of human dura and meningioma reveals cellular meningeal landscape and insights into meningioma immune response
Wang, A. Z., Bowman-Kirigin, J. A., Desai, R. R., Kang, L., Patel, P. R., Patel, B., Khan, S. M., Bender, D. E., Marlin, M. C., Liu, J., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01051-9 · Cited by 111
Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements
Baranasic, D., Hörtenhuber, M., Balwierz, P. J., Zehnder, T., Mukarram, A. K., Nepal, C., Várnai, C., Hadzhiev, Y., Jimenez-Gonzalez, A., Li, N., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01089-w · Cited by 101
Molecular diversity and evolution of neuron types in the amniote brain
Hain, D., Gallego‐Flores, T., Klinkmann, M., Macias, A., Ciirdaeva, E., Arends, A., Thum, C., Tushev, G., Kretschmer, F., Tosches, M. A., et al. (2022) · Science · doi:10.1126/science.abp8202 · Cited by 96
Integrated analysis of multimodal single-cell data with structural similarity
Cao, Y., Fu, L., Wu, J., Peng, Q., Nie, Q. H., Zhang, J., & Xie, X. S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac781 · Cited by 109
Facilitated machine learning for image-based fruit quality assessment
Knott, M., Pérez‐Cruz, F., & Defraeye, T. (2022) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2022.111401 · Cited by 121
“In this study, we used Principal Component Analysis (PCA) as a linear method and UMAP (McInnes et al., 2018) from the family of non-linear methods.”
Method
Glioblastoma epigenome profiling identifies SOX10 as a master regulator of molecular tumour subtype
Wu, Y., Fletcher, M., Gu, Z., Wang, Q., Costa, B., Bertoni, A., Man, K.-H., Schlotter, M., Felsberg, J., Mangei, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20225-w · Cited by 101
Computational Oncology in the Multi-Omics Era: State of the Art
de Anda‐Jáuregui, G., & Lemus, E. H. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00423 · Cited by 104
Automated manufacture of autologous CD19 CAR-T cells for treatment of non-hodgkin lymphoma
Jackson, Z., Roe, A., Sharma, A. A., Lopes, F. B. T. P., Talla, A., Kleinsorge-Block, S., Zamborsky, K., Schiavone, J., Manjappa, S., Schauner, R., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01941 · Cited by 102
Markers of Polyfunctional SARS-CoV-2 Antibodies in Convalescent Plasma
Natarajan, H., Crowley, A. R., Butler, S. E., Xu, S., Weiner, J. A., Bloch, E. M., Littlefield, K. E., Wieland‐Alter, W., Connor, R. I., Wright, P. F., et al. (2021) · mBio · doi:10.1128/mbio.00765-21 · Cited by 99
“0 (82) to enable dimensionality reduction of the JHMI Fc Array data set.”
Background
Computer vision tool for detection, mapping, and fault classification of photovoltaics modules in aerial IR videos
Bommes, L., Pickel, T., Buerhop‐Lutz, C., et al. (2021) · Progress in Photovoltaics Research and Applications · doi:10.1002/pip.3448 · Cited by 101
“F IGURE 14 ResNet-50 embeddings of the test dataset after dimensionality reduction with UMAP.(44) Embeddings are obtained from the last convolutional layer.”
Method
Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor
Li, Y., Zhang, L., Wang, Y., Zou, J., Yang, R., Luo, X., Wu, C., Yang, W. M., Tian, C., Xu, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34692-w · Cited by 113
“To visualizethe similarity relations between the sourcedata,the target data, and the generated data, we constructed UMAP plots 37 (umap-learn 0.4.6), which are two-dimensional representations of high-dimensional data distributions, from 3000, 1000, and 2000 randomly selected molecules from the…”
MethodInfluential
A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
Hou, W., Ji, Z., Chen, Z., Wherry, E. J., Hicks, S. C., & Ji, H. (2023) · Nature Communications · doi:10.1038/s41467-023-42841-y · Cited by 112
Data-Centric Artificial Intelligence
Jakubik, J., Vössing, M., Kühl, N., Walk, J., & Satzger, G. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-024-00857-8 · Cited by 134
“…methods that support the labeling process in terms of efﬁciency and performance (e.g., R2, E3; see Fiedler et al. 2019), meth-ods for data exploration and visualization (e.g., R1, R2; see McInnes et al. 2018), and methods to identify special data instances (e.g., R1–R6; see Northcutt et al. 2021).”
Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease
Schäffner, E., Bosch-Queralt, M., Edgar, J. M., Lehning, M., Strauß, J., Fleischer, N., Kungl, T., Wieghofer, P., Berghoff, S. A., Reinert, T., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01366-9 · Cited by 101
A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes
Colombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01167-6 · Cited by 100

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

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

UMAP: Uniform Manifold Approximation and Projection

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks