Warshanna Ahmed, Orsburn Benjamin C
The Department of Pharmacology and Molecular Sciences; The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205.
bioRxiv. 2023 Aug 29:2023.08.29.555397. doi: 10.1101/2023.08.29.555397.
Single cell proteomics (SCP) requires the analysis of dozens to thousands of single human cells to draw biological conclusions. However, assessing of the abundance of single proteins in output data presents a considerable challenge, and no simple universal solutions currently exist. To address this, we developed SCP Viz, a statistical package with a graphical user interface that can handle small and large scale SCP output from any instrument or data processing software. In this software, the abundance of individual proteins can be plotted in a variety of ways, using either unadjusted or normalized outputs. These outputs can also be transformed or imputed within the software. SCP Viz offers a variety of plotting options which can help identify significantly altered proteins between groups, both before and after quantitative transformations. Upon the discovery of subpopulations of single cells, users can easily regroup the cells of interest using straightforward text-based filters. When used in this way, SCP Viz allows users to visualize proteomic heterogeneity at the level of individual proteins, cells, or identified subcellular populations. SCP Viz is compatible with output files from MaxQuant, FragPipe, SpectroNaut, and Proteome Discoverer, and should work equally well with other formats. SCP Viz is publicly available at https://github.com/orsburn/SCPViz. For demonstrations, users can download our test data from GitHub and use an online version that accepts user input for analysis at https://orsburnlab.shinyapps.io/SCPViz/.
单细胞蛋白质组学(SCP)需要分析数十到数千个人类单细胞才能得出生物学结论。然而,评估输出数据中单个蛋白质的丰度是一项相当大的挑战,目前尚无简单通用的解决方案。为了解决这个问题,我们开发了SCP Viz,这是一个带有图形用户界面的统计软件包,可处理来自任何仪器或数据处理软件的小规模和大规模SCP输出。在该软件中,可以使用未调整或归一化的输出以多种方式绘制单个蛋白质的丰度。这些输出也可以在软件内进行转换或估算。SCP Viz提供了多种绘图选项,有助于识别定量转换前后组间显著变化的蛋白质。一旦发现单细胞亚群,用户可以使用简单的基于文本的过滤器轻松地对感兴趣的细胞进行重新分组。以这种方式使用时,SCP Viz允许用户在单个蛋白质、细胞或已识别的亚细胞群体水平上可视化蛋白质组的异质性。SCP Viz与MaxQuant、FragPipe、SpectroNaut和Proteome Discoverer的输出文件兼容,并且应该与其他格式同样兼容。SCP Viz可在https://github.com/orsburn/SCPViz上公开获取。如需演示,用户可以从GitHub下载我们的测试数据,并使用在https://orsburnlab.shinyapps.io/SCPViz/上接受用户输入进行分析的在线版本。