美国耶鲁大学Smita Krishnaswamy、乔治亚大学Natalia B. Ivanova、加拿大蒙特利尔大学Guy Wolf等研究人员合作开发了能够可视化高维生物数据中结构和转换的工具。相关论文发表在2019年12月3日出版的《自然—生物技术》上。

由高通量技术创建的高维数据需要可视化工具，以直观的方式显示数据结构和模式。研究人员提出了一种称为PHATE的可视化方法，其使用数据点之间的信息几何距离来捕获局部和全局非线性结构。研究人员将PHATE与各种人工和生物学数据集上的其他工具进行了比较，发现与其他工具相比，PHATE能够始终如一地保留数据的一系列模式，包括连续进行、分支和聚类。研究人员定义了流形保留度量，这被称为去噪流形保留（DEMaP），并表明PHATE产生的低维嵌入与现有的可视化方法相比，在量化上具有更好的去噪效果。对有关人类生殖层分化的新生成的单细胞RNA测序数据集的分析表明，PHATE如何揭示对主要发育分支的独特生物学见解，包括鉴定三个先前未报道的亚群。研究人员还发现，PHATE适用于多种数据类型，包括大量细胞计数、单细胞RNA测序、Hi-C和肠道微生物组数据。

附：英文原文

Title: Visualizing structure and transitions in high-dimensional biological data

Author: Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, Antonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, Smita Krishnaswamy

Issue&Volume: 2019-12-03

Abstract: 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.

DOI: 10.1038/s41587-019-0336-3

Source: https://www.nature.com/articles/s41587-019-0336-3