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Visualizing Structure and Transitions for Biological Data Exploration

Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel 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
Department of GeneticsApplied Mathematics Program;Department of Computer Science
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David van Dijk
Department of GeneticsDepartment of Computer Science
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Zheng Wang
Yale Stem Cell Center, Department of Genetics, Yale University, New Haven, CT, USA
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Scott Gigante
Department of Genetics
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Daniel Burkhardt
Department of Genetics
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William S. Chen
Department of Genetics
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Kristina Yim
Department of Genetics
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Antonia van den Elzen
Department of Genetics
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Matthew J. Hirn
Department of Computational Mathematics, Science and EngineeringDepartment of Mathematics, Michigan State University, East Lansing, MI, USA
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Ronald R. Coifman
Applied Mathematics Program;
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Natalia B. Ivanova
Yale Stem Cell Center, Department of Genetics, Yale University, New Haven, CT, USA
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  • For correspondence: natalia.ivanova@yale.edu
Guy Wolf
Applied Mathematics Program;
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Smita Krishnaswamy
Department of GeneticsDepartment of Computer Science
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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-geometry 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 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. Finally, we use PHATE to explore 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.

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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 June 28, 2018.
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Visualizing Structure and Transitions for Biological Data Exploration
Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel 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 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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