Empirical Bayes Analysis of Quantitative Proteomics Experiments
Empirical Bayes Analysis of Quantitative Proteomics Experiments
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DOI：
10.1371/journal.pone.0007454
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发表时间：
2009-10-14
期刊：
影响因子：
3.7
通讯作者：
Golub, Todd R.
中科院分区：
文献类型：
--
作者：
Margolin, Adam A.;Ong, Shao-En;Golub, Todd R.
背景：基于质谱的蛋白质组学的进展使得蛋白质组学数据能够并入生物学的系统方法中。然而，分析方法的发展却相对滞后。在这里，我们描述了定量蛋白质组学数据分析的经验贝叶斯框架。该方法提供了每个实验的统计描述，包括2个样品之间的丰度不同的蛋白质的数量，实验的统计能力来检测它们，以及每个蛋白质的假阳性概率。方法/主要发现：我们分析了2种类型的质谱实验。首先，我们证明了该方法在亲和纯化实验中以高精度鉴定了小分子的蛋白质靶标。其次，我们重新分析了一个质谱数据集，旨在识别由microRNA调控的蛋白质。我们的结果得到了预测靶基因的3' UTR区域的序列分析的支持，并且我们发现以前报道的结论，即大部分蛋白质组由microRNA调控，并没有得到我们对数据的统计分析的支持。我们的研究结果强调了对蛋白质组数据进行严格统计分析的重要性，并且这里所描述的方法提供了一种统计框架来鲁棒地和可靠地解释这种数据。
Background: Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.Methodology/Principal Findings: We analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3' UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data.Conclusions/Significance: Our results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.