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Visualizing structure and transitions in high-dimensional biological data
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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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“Another approach is dimensionality reduction, including novel methods such as PHATE and Multiscale PHATE, which can learn abstract representations of biological and clinical data at different levels of granularity, and have been shown to predict clinical outcomes, for example, in people with coronavirus disease 2019 (COVID-19) 21,22 .”
From Multimodal biomedical AI (2022) · cites this paper as [21]
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“We begin by modeling the cellular transcriptomic state space as a smooth, low-dimensional manifold or set of manifolds.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [3]
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Self-citation
Metadata
- 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
- Venue
- Nature Biotechnology
- Published
- 2019-12
- Volume
- 37
- Issue
- 12
- Pages
- 1482-1492
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- 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
- 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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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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