主要工作：We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. 

横向比较：We compare PHATE to 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, better than other tools.

Fig. 1b, Left, a 2D drawing of an artificial tree with color-coded branches. Right, comparison of PCA, t-SNE and the PHATE visualizations for the high-dimensional artificial tree data.

Fig. 1c, Comparison of PCA, t-SNE and the PHATE visualizations for new EB data.

具体评价：We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods.

实例演示：An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations.

Fig. 6 | PHATE analysis of embryoid body scRNA-seq data with n = 16,825 cells.

应用拓展：We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.

Fig. 1d, PHATE applied to various datatypes. Left, PHATE on human microbiome data. Middle, PHATE on Hi-C chromatin conformation data. Right, PHATE on iPSC CyTOF data.