Stein David F, Chen Huidong, Vinyard Michael E, Qin Qian, Combs Rebecca D, Zhang Qian, Pinello Luca
Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
Front Genet. 2021 Oct 28;12:764170. doi: 10.3389/fgene.2021.764170. eCollection 2021.
Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present , a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.
单细胞分析已经改变了我们对细胞群体内异质性进行建模的能力。随着这些分析在测量细胞分子过程各个方面的能力不断提升,用于分析和有意义地可视化此类数据的计算方法也需要相应的创新。独立来看,虚拟现实(VR)最近已成为一种强大的技术,可用于动态探索复杂数据,并有望适应单细胞数据可视化中的挑战。然而,迄今为止,采用VR进行单细胞数据可视化受到昂贵的前置硬件或先进的数据预处理技能的阻碍。为了解决当前的不足,我们展示了 ,这是一个用于可视化单细胞数据的用户友好型网络应用程序,专为廉价且易于获得的虚拟现实硬件(例如,谷歌纸板,约8美元)设计。 可以可视化来自各种基于测序的技术的数据,包括转录组学、表观基因组学和蛋白质组学数据以及它们的组合。支持的分析模式包括聚类方法以及轨迹推断和通过建模RNA速度发现的动态变化的可视化。我们提供了一个配套软件包, 以简化从最广泛采用的单细胞分析工具进行的数据转换,以及一个不断增长的预分析数据集数据库,用户可以向该数据库贡献数据。