%0 Journal Article %A Kevin R. Moon %A David van Dijk %A Zheng Wang %A Scott Gigante %A Daniel Burkhardt %A William S. Chen %A Kristina Yim %A Antonia van den Elzen %A Matthew J. Hirn %A Ronald R. Coifman %A Natalia B. Ivanova %A Guy Wolf %A Smita Krishnaswamy %T Visualizing Structure and Transitions for Biological Data Exploration %D 2018 %R 10.1101/120378 %J bioRxiv %P 120378 %X 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. %U https://www.biorxiv.org/content/biorxiv/early/2018/06/28/120378.full.pdf