A Bioconductor workflow for processing and analysing spatial proteomics data.
A Bioconductor workflow for processing and analysing spatial proteomics data.
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DOI：
10.12688/f1000research.10411.2
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Gatto L
中科院分区：
文献类型：
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作者：
Breckels LM;Mulvey CM;Lilley KS;Gatto L
空间蛋白质组学是对蛋白质亚细胞定位的系统研究。在这个工作流程中，我们描述了使用MSnbase和pRoloc BioConductor软件包对一个典型的基于定量质谱学的空间蛋白质组实验的分析。为了引导用户通过计算管道，我们使用了最近发表的一项实验，预测了多能胚胎小鼠干细胞中的蛋白质亚细胞定位。我们描述了手头的软件基础设施、数据导入和处理、质量控制、亚细胞标记定义、可视化和交互探索。然后，我们演示了统计学习方法的应用和解释，包括使用半监督学习、分类、聚类和转移学习进行新颖性检测，并通过数据导出结束了管道。该工作流程针对的是一般熟悉蛋白质组学，特别是空间蛋白质组学的初学者。
Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.