Visualizing Structure and Transitions for Biological Data Exploration
Kevin R Moon,
David van Dijk,
Scott A. Gigante,
Daniel B. Burkhardt,
William S. Chen,
Antonia van den Elzen,
Matthew J Hirn,
Ronald R. Coifman,
Natalia B Ivanova,
Posted 24 Mar 2017
bioRxiv DOI: 10.1101/120378
Posted 24 Mar 2017
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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