Visualizing structure and transitions in high-dimensional biological data

• Published in: Nature biotechnology Vol. 37; no. 12; pp. 1482 - 1492
• Main Authors: Moon, Kevin R., van Dijk, David, Wang, Zheng, Gigante, Scott, Burkhardt, Daniel B., Chen, William S., Yim, Kristina, Elzen, Antonia van den, Hirn, Matthew J., Coifman, Ronald R., Ivanova, Natalia B., Wolf, Guy, Krishnaswamy, Smita
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2019; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2164; Agriculture; Algorithms; Analysis; Animals; Big Data; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cell Differentiation; Cells, Cultured; Computer Simulation; Cytometry; Data points; Data structures; Databases, Genetic; Datasets; Embedding; Gastrointestinal Microbiome; Gene sequencing; Genomics - methods; High-Throughput Screening Assays - methods; Humans; Image Processing, Computer-Assisted - methods; Intestinal microflora; Life Sciences; Manifolds (mathematics); Mice; Microbiomes; Noise reduction; Preservation; Progressions; Ribonucleic acid; RNA; RNA sequencing; Sequence Analysis, RNA; Single-Cell Analysis; Subpopulations; Visualization; Visualization (Computers)
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/s41587-019-0336-3

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. 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. 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. 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. 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. PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction.