Cakir Batuhan, Prete Martin, Huang Ni, van Dongen Stijn, Pir Pinar, Kiselev Vladimir Yu
Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.
Gebze Technical University, Department of Bioengineering, Gebze, Kocaeli, 41400, Turkey.
NAR Genom Bioinform. 2020 Sep;2(3):lqaa052. doi: 10.1093/nargab/lqaa052. Epub 2020 Jul 29.
In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a single cell to millions of cells. Due to its high dimensionality, it is not always feasible to visualize scRNAseq data and share it in a scientific report or an article publication format. Recently, many interactive analysis and visualization tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review several of the currently available scRNAseq visualization tools and benchmark the subset that allows to visualize the data on the web and share it with others. We consider the memory and time required to prepare datasets for sharing as the number of cells increases, and additionally review the user experience and features available in the web interface. To address the problem of format compatibility we have also developed a user-friendly R package, , which allows users to convert their own scRNAseq datasets into a specific data format for visualization.
在过去十年中，单细胞RNA测序（scRNAseq）数据集的规模已从单个细胞增长到数百万个细胞。由于其高维度性，以科学报告或文章发表的形式可视化scRNAseq数据并进行分享并不总是可行的。最近，已经开发了许多交互式分析和可视化工具来解决这个问题，并促进科学界的知识传播。在本研究中，我们回顾了几种当前可用的scRNAseq可视化工具，并对允许在网络上可视化数据并与他人共享的子集进行了基准测试。我们考虑随着细胞数量增加为共享准备数据集所需的内存和时间，并额外回顾了网络界面中的用户体验和可用功能。为了解决格式兼容性问题，我们还开发了一个用户友好的R包，它允许用户将自己的scRNAseq数据集转换为特定的数据格式进行可视化。
