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Visualizing Structure and Transitions for Biological Data Exploration

Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, Antonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, Smita Krishnaswamy
doi: https://doi.org/10.1101/120378
Kevin R. Moon
1Department of Genetics, Yale University, New Haven, CT, USA
2Applied Mathematics Program, Yale University, New Haven, CT, USA
3Department of Computer ScienceYale University, New Haven, CT, USA
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David van Dijk
1Department of Genetics, Yale University, New Haven, CT, USA
3Department of Computer ScienceYale University, New Haven, CT, USA
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Zheng Wang
4Yale Stem Cell Center, Department of Genetics, Yale University, New Haven, CT, USA
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Scott Gigante
1Department of Genetics, Yale University, New Haven, CT, USA
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Daniel B. Burkhardt
1Department of Genetics, Yale University, New Haven, CT, USA
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William S. Chen
1Department of Genetics, Yale University, New Haven, CT, USA
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Kristina Yim
1Department of Genetics, Yale University, New Haven, CT, USA
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Antonia van den Elzen
1Department of Genetics, Yale University, New Haven, CT, USA
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Matthew J. Hirn
5Department of Computational Mathematics, Science and Engineering, East Lansing, MI, USA
6Department of Mathematics, Michigan State University, East Lansing, MI, USA
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Ronald R. Coifman
2Applied Mathematics Program, Yale University, New Haven, CT, USA
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Natalia B. Ivanova
4Yale Stem Cell Center, Department of Genetics, Yale University, New Haven, CT, USA
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  • For correspondence: natalia.ivanova@yale.edu
Guy Wolf
2Applied Mathematics Program, Yale University, New Haven, CT, USA
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Smita Krishnaswamy
1Department of Genetics, Yale University, New Haven, CT, USA
3Department of Computer ScienceYale University, New Haven, CT, USA
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  • For correspondence: smita.krishnaswamy@yale.edu
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Abstract

With the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometric distance between datapoints. We perform extensive comparison between PHATE and 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. We define a manifold preservation metric DEMaP to show that PHATE produces quantitatively better denoised embeddings than existing visualization methods. We show that PHATE is able to gain unique insight from a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail, including the identification of three novel subpopulations. Finally, we show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 04, 2019.
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Visualizing Structure and Transitions for Biological Data Exploration
Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, Antonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, Smita Krishnaswamy
bioRxiv 120378; doi: https://doi.org/10.1101/120378
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Visualizing Structure and Transitions for Biological Data Exploration
Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, Antonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, Smita Krishnaswamy
bioRxiv 120378; doi: https://doi.org/10.1101/120378

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