小柯机器人

美国麻省理工学院和哈佛大学广泛研究所Hyunghoon Cho和Bonnie Berger课题组合作在研究中取得进展。他们的最新研究表明通过保留单细胞转录组数据密度可视化可评估其差异。相关论文发表在2021年1月18日出版的《自然-生物技术》杂志上。

研究人员介绍了den-SNE和densMAP，它们分别是基于t分布随机邻居嵌入(t-SNE)和统一流形逼近与投影（UMAP）的可视化工具，但保留了其密度集；研究人员展示了其将有关转录组差异信息准确整合到单细胞RNA测序数据可视化过程中的能力。

研究人员将其应用到最近发布的数据集，发现其可以揭示一系列生物学过程中转录组差异的显著变化，包括血液和肿瘤中免疫细胞转录组差异的异质性、人类免疫细胞的专一性和秀丽隐杆线虫的发育轨迹。该方法还可灵活应用于可视化其他学科领域中的高维数据。

研究人员表示，非线性数据可视化方法（例如t-SNE和UMAP）揭示了二维或三维体系中单个细胞的复杂转录组动态，但忽略了局部密度原始空间中的数据点，这通常会产生误导性可视化，在这种情况下数据集中的细胞子集赋予了比其在转录多样性数据集中更多的视觉空间。

附：英文原文

Title: Assessing single-cell transcriptomic variability through density-preserving data visualization

Author: Ashwin Narayan, Bonnie Berger, Hyunghoon Cho

Issue&Volume: 2021-01-18

Abstract: Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains. den-SNE and densMAP enhance single-cell transcriptomic data visualization by incorporating density information.

DOI: 10.1038/s41587-020-00801-7

Source: https://www.nature.com/articles/s41587-020-00801-7

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex