Visualizing structure and transitions in high-dimensional biological data
Visualizing structure and transitions in high-dimensional biological data
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DOI：
10.1038/s41587-019-0336-3
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发表时间：
2019-12-01
影响因子：
46.9
通讯作者：
Krishnaswamy, Smita
中科院分区：
文献类型：
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作者：
Moon, Kevin R.;van Dijk, David;Krishnaswamy, Smita
由高吞吐量技术创建的高维数据需要可视化工具，以直观的形式揭示数据结构和模式。我们提出了PHATE，一种可视化方法，捕捉本地和全球的非线性结构，使用数据点之间的信息几何距离。我们将PHATE与其他工具在各种人工和生物数据集上进行比较，发现它始终保留了一系列数据模式，包括连续进展，分支和集群，优于其他工具。我们定义了一个流形保存度量，我们称之为降噪嵌入流形保存（DEMaP），并表明PHATE产生的低维嵌入，与现有的可视化方法相比，更好地量化降噪。对新生成的关于人类生殖层分化的单细胞RNA测序数据集的分析展示了PHATE如何揭示对主要发育分支的独特生物学见解，包括识别三个以前未描述的亚群。我们还表明，PHATE适用于各种数据类型，包括质谱仪，单细胞RNA测序，Hi-C和肠道微生物组数据。
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.