Visualizing structure and transitions in high-dimensional biological data
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...
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| Published in | Nature biotechnology Vol. 37; no. 12; pp. 1482 - 1492 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.12.2019
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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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.
PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction. |
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| AbstractList | 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. 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. PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction. 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. PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction. 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 datapoints. We compared 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 do other tools. We define a manifold preservation metric called ‘Denoised Embedding Manifold Preservation’ (DEMaP) and show that PHATE produces quantitatively better denoised lower-dimensional embeddings compared with existing visualization methods. An analysis of a newly generated scRNA-seq 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. |
| Audience | Academic |
| Author | Burkhardt, Daniel B. Chen, William S. van Dijk, David Wang, Zheng Yim, Kristina Wolf, Guy Ivanova, Natalia B. Moon, Kevin R. Elzen, Antonia van den Coifman, Ronald R. Krishnaswamy, Smita Hirn, Matthew J. Gigante, Scott |
| AuthorAffiliation | 3 Department of Computer Science, Yale University, New Haven, Connecticut, USA 6 Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut, USA 11 Department of Mathematics and Statistics, Université de Montréal, Montréal, Quebec, Canada 9 Department of Mathematics, Michigan State University, East Lansing, Michigan, USA 8 Department of Computational Mathematics, Science and Engineering, East Lansing, Michigan, USA 10 Department of Genetics, Center for Molecular Medicine, University of Georgia, Athens, Georgia, USA 7 Applied Mathematics Program, Yale University, New Haven, Connecticut, USA 2 Department of Genetics, Yale University, New Haven, Connecticut, USA 4 Department of Internal Medicine, Cardiovascular Research Center, section Cardiology, Yale University, New Haven, Connecticut, USA 5 Department of Genetics, Yale Stem Cell Center, Yale University, New Haven, Connecticut, USA 1 Department of Mathematics and Statistics, Utah State University, Logan, Utah, USA 12 |
| AuthorAffiliation_xml | – name: 1 Department of Mathematics and Statistics, Utah State University, Logan, Utah, USA – name: 11 Department of Mathematics and Statistics, Université de Montréal, Montréal, Quebec, Canada – name: 3 Department of Computer Science, Yale University, New Haven, Connecticut, USA – name: 2 Department of Genetics, Yale University, New Haven, Connecticut, USA – name: 9 Department of Mathematics, Michigan State University, East Lansing, Michigan, USA – name: 4 Department of Internal Medicine, Cardiovascular Research Center, section Cardiology, Yale University, New Haven, Connecticut, USA – name: 10 Department of Genetics, Center for Molecular Medicine, University of Georgia, Athens, Georgia, USA – name: 6 Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut, USA – name: 12 Mila - Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada – name: 5 Department of Genetics, Yale Stem Cell Center, Yale University, New Haven, Connecticut, USA – name: 7 Applied Mathematics Program, Yale University, New Haven, Connecticut, USA – name: 8 Department of Computational Mathematics, Science and Engineering, East Lansing, Michigan, USA |
| Author_xml | – sequence: 1 givenname: Kevin R. surname: Moon fullname: Moon, Kevin R. organization: Department of Mathematics and Statistics, Utah State University – sequence: 2 givenname: David orcidid: 0000-0003-3911-9925 surname: van Dijk fullname: van Dijk, David organization: Cardiovascular Research Center, section Cardiology, Department of Internal Medicine, Yale University, Department of Computer Science, Yale University – sequence: 3 givenname: Zheng surname: Wang fullname: Wang, Zheng organization: School of Basic Medicine, Qingdao University, Yale Stem Cell Center, Department of Genetics, Yale University – sequence: 4 givenname: Scott orcidid: 0000-0002-4544-2764 surname: Gigante fullname: Gigante, Scott organization: Computational Biology and Bioinformatics Program, Yale University – sequence: 5 givenname: Daniel B. orcidid: 0000-0001-7744-1363 surname: Burkhardt fullname: Burkhardt, Daniel B. organization: Department of Genetics, Yale University – sequence: 6 givenname: William S. surname: Chen fullname: Chen, William S. organization: Department of Genetics, Yale University – sequence: 7 givenname: Kristina surname: Yim fullname: Yim, Kristina organization: Department of Genetics, Yale University – sequence: 8 givenname: Antonia van den surname: Elzen fullname: Elzen, Antonia van den organization: Department of Genetics, Yale University – sequence: 9 givenname: Matthew J. orcidid: 0000-0003-0290-4292 surname: Hirn fullname: Hirn, Matthew J. organization: Department of Computational Mathematics, Science and Engineering, Michigan State University, Department of Mathematics, Michigan State University – sequence: 10 givenname: Ronald R. surname: Coifman fullname: Coifman, Ronald R. organization: Applied Mathematics Program, Yale University – sequence: 11 givenname: Natalia B. orcidid: 0000-0002-6494-6247 surname: Ivanova fullname: Ivanova, Natalia B. email: natalia.ivanova@uga.edu organization: Department of Genetics, Center for Molecular Medicine, University of Georgia – sequence: 12 givenname: Guy orcidid: 0000-0002-6740-059X surname: Wolf fullname: Wolf, Guy email: guy.wolf@umontreal.ca organization: Department of Mathematics and Statistics, Université de Montréal, Mila—Quebec Artificial Intelligence Institute – sequence: 13 givenname: Smita orcidid: 0000-0001-5823-1985 surname: Krishnaswamy fullname: Krishnaswamy, Smita email: smita.krishnaswamy@yale.edu organization: Department of Computer Science, Yale University, Department of Genetics, Yale University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31796933$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally. KM, SK, GW, and DD envisioned the project. KM, DD, SG, and GW implemented the method. KM, DD, SG, SK, and NI performed the analyses. KM, SK, GW, and NI wrote the paper. DD, SG, and DB assisted in writing. DB, WC, and KY assisted in the analysis. KM, GW, MH, and RC developed the mathematical foundations of the method. ZW, AE, and NI were responsible for data acquisition and processing. Author Contributions |
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| Snippet | The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We... |
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| SubjectTerms | 631/114/1305 631/114/2164 Agriculture Algorithms Analysis Animals Big Data Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cell Differentiation Cells, Cultured Computer Simulation Cytometry Data points Data structures Databases, Genetic Datasets Embedding Gastrointestinal Microbiome Gene sequencing Genomics - methods High-Throughput Screening Assays - methods Humans Image Processing, Computer-Assisted - methods Intestinal microflora Life Sciences Manifolds (mathematics) Mice Microbiomes Noise reduction Preservation Progressions Ribonucleic acid RNA RNA sequencing Sequence Analysis, RNA Single-Cell Analysis Subpopulations Visualization Visualization (Computers) |
| Title | Visualizing structure and transitions in high-dimensional biological data |
| URI | https://link.springer.com/article/10.1038/s41587-019-0336-3 https://www.ncbi.nlm.nih.gov/pubmed/31796933 https://www.proquest.com/docview/2320981885 https://search.proquest.com/docview/2321665098 https://pubmed.ncbi.nlm.nih.gov/PMC7073148 |
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