RT Journal Article
SR Electronic
T1 Visualizing Structure and Transitions for Biological Data Exploration
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 120378
DO 10.1101/120378
A1 Kevin R. Moon
A1 David van Dijk
A1 Zheng Wang
A1 Scott Gigante
A1 Daniel Burkhardt
A1 William S. Chen
A1 Kristina Yim
A1 Antonia van den Elzen
A1 Matthew J. Hirn
A1 Ronald R. Coifman
A1 Natalia B. Ivanova
A1 Guy Wolf
A1 Smita Krishnaswamy
YR 2018
UL http://biorxiv.org/content/early/2018/06/28/120378.abstract
AB 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.