RT Journal Article
SR Electronic
T1 PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 120378
DO 10.1101/120378
A1 Moon, Kevin R.
A1 van Dijk, David
A1 Wang, Zheng
A1 Chen, William
A1 Hirn, Matthew J.
A1 Coifman, Ronald R.
A1 Ivanova, Natalia B.
A1 Wolf, Guy
A1 Krishnaswamy, Smita
YR 2017
UL http://biorxiv.org/content/early/2017/03/24/120378.abstract
AB In recent years, dimensionality reduction methods have become critical for visualization, exploration, and interpretation of high-throughput, high-dimensional biological data, as they enable the extraction of major trends in the data while discarding noise. However, biological data contains a type of predominant structure that is not preserved in commonly used methods such as PCA and tSNE, namely, branching progression structure. This structure, which is often non-linear, arises from underlying biological processes such as differentiation, graded responses to stimuli, and population drift, which generate cellular (or population) diversity. We propose a novel, affinity-preserving embedding called PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding), designed explicitly to preserve progression structure in data.PHATE provides a denoised, two or three-dimensional visualization of the complete branching trajectory structure in high-dimensional data. It uses heat-diffusion processes, which naturally denoise the data, to compute cell-cell affinities. Then, PHATE creates a diffusion-potential geometry by free-energy potentials of these processes. This geometry captures high-dimensional trajectory structures, while enabling a natural embedding of the intrinsic data geometry. This embedding accurately visualizes trajectories and data distances, without requiring strict assumptions typically used by path-finding and tree-fitting algorithms, which have recently been used for pseudotime orderings or tree-renderings of cellular data. Furthermore, PHATE supports a wide range of data exploration tasks by providing interpretable overlays on top of the visualization. We show that such overlays can emphasize and reveal trajectory end-points, branch points and associated split-decisions, progression-forming variables (e.g., specific genes), and paths between developmental events in cellular state-space. We demonstrate PHATE on single-cell RNA sequencing and mass cytometry data pertaining to embryoid body differentiation, IPSC reprogramming, and hematopoiesis in the bone marrow. We also demonstrate PHATE on non-single cell data including single-nucleotide polymorphism (SNP) measurements of European populations, and 16s sequencing of gut microbiota.