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)

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...