General Statistical Framework for Quantitative Proteomics by Stable Isotope Labeling

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.稳定同位素标记（stable isotope labeling, SIL）与质谱（mass spectrometry, MS）联用，可实现复杂混合物中数千种蛋白质的丰度比较。然而，由于缺乏合适的统计标准，对这些技术产生的大型数据集进行解读仍是一项挑战。本文提出了一种通用适用模型，可准确阐释当前主流SIL方法（包括18O标记、同重同位素相对和绝对定量（iTRAQ）标记以及细胞培养稳定同位素氨基酸标记（SILAC））与不同质谱仪所获数据的表现特征。该模型将总技术变异分解为光谱级、肽段级与蛋白质级变异分量，并通过将48组实验分布与18种不同原假设进行比对，验证了其普适有效性。除具备普适性外，该算法的性能至少不逊于其他现有方法。此外，该模型还提供了一套可完整整合定量信息与误差信息的通用框架，能够对不同SIL实验的结果开展对比分析。我们将该模型应用于酵母经低浓度过氧化氢（H₂O₂）处理后诱导的蛋白质变化全局分析，证实了通过严谨的数据整合可获得更高的统计效力。本研究结果凸显了为高通量数据分析建立一套完善且经过验证的统计框架的重要性。