Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.
Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.
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DOI：
10.1007/978-1-0716-1967-4_8
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发表时间：
2023-01-01
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影响因子：
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通讯作者：
Dowsey, Andrew W
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作者：
Phillips, Alexander M;Unwin, Richard D;Dowsey, Andrew W
seaMass是一个R软件包，用于在肽鉴定、蛋白质分组和特征水平定量后对蛋白质组学质谱数据进行蛋白质水平定量、标准化和差异表达分析。使用封闭实验设计的概念，seaMass可以分析所有常见的发现蛋白质组学范式，包括无标记（例如，沃茨基因组输入）、SILAC（例如，MaxQuant输入）、同位素标记（例如，SCIEX ProteinPilot iTraq和Thermo ProteomeDiscoverer TMT输入），以及数据独立采集（例如，OpenSWATH-PyProphet输入），并且能够扩展到数百个或更多的分析。通过利用分层贝叶斯建模，seaMass评估了各检测试剂中每个特征和肽的定量可靠性，因此只有一致的特征和肽才能强烈影响所得蛋白质组的定量。类似地，捕获每个单独测定中的无法解释的变化，提供用于质量控制的度量和可疑测定的自动降权。为了实现这一点，seaMass输出的每个蛋白质组水平定量都伴随着其后验不确定性的标准差。此外，seaMass集成了一个灵活的差异表达分析子系统，具有基于流行的MCMCCglmm包的错误发现率控制，用于贝叶斯混合效应建模，还提供了不确定性感知的主成分分析。我们提供了使用seaMass进行端到端分析的描述，该分析使用与已发表的临床蛋白质组学研究相关的真实的数据集。
seaMass is an R package for protein-level quantification, normalization, and differential expression analysis of proteomics mass spectrometry data after peptide identification, protein grouping, and feature-level quantification. Using the concept of a blocked experimental design, seaMass can analyze all common discovery proteomics paradigms, including label-free (e.g., Waters Progenesis input), SILAC (e.g., MaxQuant input), isotope labelling (e.g., SCIEX ProteinPilot iTraq and Thermo ProteomeDiscoverer TMT input), and data-independent acquisition (e.g., OpenSWATH-PyProphet input), and is able to scale to study with hundreds of assays or more. By utilizing hierarchical Bayesian modelling, seaMass assesses the quantification reliability of each feature and peptide across assays so that only those in consensus influence the resulting protein group quantification strongly. Similarly, unexplained variation in each individual assay is captured, providing both a metric for quality control and automatic down-weighting of suspect assays. To achieve this, each protein group-level quantification outputted by seaMass is accompanied by the standard deviation of its posterior uncertainty. Moreover, seaMass integrates a flexible differential expression analysis subsystem with false discovery rate control based on the popular MCMCglmm package for Bayesian mixed-effects modelling, and also provides uncertainty-aware principal components analysis. We provide a description for using seaMass to perform an end-to-end analysis using a real dataset associated with a published clinical proteomics study.