可视化
计算机科学
多样性(控制论)
嵌入
非线性降维
歧管(流体力学)
公制(单位)
鉴定(生物学)
生物学数据
计算生物学
数据挖掘
生物
理论计算机科学
人工智能
生物信息学
降维
生态学
工程类
机械工程
经济
运营管理
作者
Kevin R. Moon,David van Dijk,Zheng Wang,Scott Gigante,Daniel Burkhardt,William S. Chen,Kristina Yim,Antonia van den Elzen,Matthew Hirn,Ronald R. Coifman,Natalia Ivanovа,Guy Wolf,Smita Krishnaswamy
标识
DOI:10.1038/s41587-019-0336-3
摘要
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.
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