Abstract：
Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.
摘要：
基于质谱（MS）的单细胞蛋白质组学（SCP）通过关注细胞蛋白质的功能效应子来探索细胞异质性。然而,从MS数据中提取有意义的生物信息绝非易事，尤其是单细胞。目前,数据分析工作流程从一个研究团队到另一个研究团队有很大的不同。此外，由于缺乏地面真相，很难评估管道。我们的团队开发了名为scp的R/Bioconductor软件包，为SCP数据分析提供了一个标准化的框架。它依赖于广泛使用的QFeatures和SingleCellExperiment数据结构。此外,我们使用包含以已知比例混合的细胞系的设计,以产生受控的变异性用于数据分析基准.在这一章中,我们使用scp软件包为SCP数据提供了灵活的数据分析协议,并在处理的每个步骤中提供了全面的解释.我们的主要步骤是功能和细胞水平的质量控制，将原始数据汇总为肽和蛋白质，归一化，和批量更正。我们使用我们的地面实况数据集验证我们的工作流程。我们说明如何使用这个模块化，标准化框架，并强调一些关键步骤。
