Cited by 1,065Single-cell and spatial transcriptomicsTopological and Geometric Data AnalysisData Visualization and Analytics

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Visualizing structure and transitions in high-dimensional biological data

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Moon, Kevin R., van Dijk, David, Wang, Zheng, Gigante, Scott, Burkhardt, Daniel B., Chen, William S., Yim, Kristina, Elzen, Antonia van den, Hirn, Matthew J., Coifman, Ronald R., Ivanova, Natalia B., Wolf, Guy, Krishnaswamy, Smita·2019·Nature Biotechnology

DOI 10.1038/s41587-019-0336-3PMC PMC7073148

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Abstract

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.

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Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Diffusion maps
Coifman, R. R., & Lafon, S. (2006) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2006.04.006 · Cited by 2,380
Self-citation

Show all 79 references

Research works printed but not linked: 4

Moon, T. K. & Stirling, W. C. Mathematical Methods and Algorithms for Signal Processing (Prentice Hall, 2000).
Butterworth, S. On the theory of filter amplifiers. Wireless Engineer 7, 536–541 (1930).
Salicrú, M., Sanchez, A., Conde, J. & Sanchez, P. Entropy measures associated with K and M divergences. Soochow J. Math. 21, 291–298 (1995).
Yang, T., Liu, J., McMillan, L. & Wang, W. A fast approximation to multidimensional scaling. In Proc. IEEE Workshop on Computation Intensive Methods for Computer Vision (IEEE, 2006).

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Castro, E., Benz, A., Tong, A., Wolf, G., & Krishnaswamy, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06885 · Cited by 2
Self-citation
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Pang, J., Qiu, P. S., He, Y., Deng, Y., Tang, W., Huang, Z., Yan, J., Li, B., Lin, A., Cao, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.29.696804 · Cited by 2

Authors: Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., Ivanova, N. B., Wolf, G., & Krishnaswamy, S.
Affiliations: Utah State University (US)
Qingdao University (CN)
Yale University (US)
Michigan State University (US)
University of Georgia (US)
Mila - Quebec Artificial Intelligence Institute (CA)
Corresponding author: Ivanova, Natalia B.
Wolf, Guy
Krishnaswamy, Smita
ORCID: Moon, Kevin R.
van Dijk, David
Wang, Zheng
Gigante, Scott
Burkhardt, Daniel B.
Chen, William S.
Hirn, Matthew J.
Coifman, Ronald R.
Ivanova, Natalia B.
Wolf, Guy
Krishnaswamy, Smita
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2019-12
Volume: 37
Issue: 12
Pages: 1482-1492
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-019-0336-3
PMC: PMC7073148
OpenAlex: W2993894543
Cited by: 1,065
References: 79
Citations by year: 2026: 167 · 2025: 213 · 2024: 175 · 2023: 166 · 2022: 141 · 2021: 133 · 2020: 58 · 2019: 10
FWCI: 28.38
Type: journal article
Language: en
Update notices: correction (2020-01-02)
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Gruber Foundation
U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (F31HD097958)
Alfred P. Sloan Foundation (FG-2016-6607)
United States Department of Defense | Defense Advanced Research Projects Agency (D16AP00117)
U.S. Department of Health & Human Services | National Institutes of Health (1R01HG008383, R01GM107092, R01GM130847)
l’institut de valorisation des donnees
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
Keywords: PHATE, high-dimensional data visualization, nonlinear structures, single-cell RNA sequencing, developmental trajectories, subpopulation identification, mass cytometry, Hi-C, gut microbiota, high-throughput biological data
MeSH terms: Gastrointestinal Microbiome, Big Data, Algorithms, Animals, Cell Differentiation, Cells, Cultured, Computer Simulation, Humans, Image Processing, Computer-Assisted, Sequence Analysis, RNA, Genomics, Databases, Genetic, Mice, High-Throughput Screening Assays, Single-Cell Analysis

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