Eckel-Passow Jeanette E, Mahoney Douglas W, Oberg Ann L, Zenka Roman M, Johnson Kenneth L, Nair K Sreekumaran, Kudva Yogish C, Bergen H Robert, Therneau Terry M
Division of Biomedical Statistics and Informatics.
J Proteomics Bioinform. 2010 Dec 15;3(12):314-320. doi: 10.4172/jpb.1000158.
Interpreting and quantifying labeled mass-spectrometry data is complex and requires automated algorithms, particularly for large scale proteomic profiling. Here, we propose the use of bi-linear regression to quantify relative abundance across the elution profile in a unified model. The bi-linear regression model takes advantage of the fact that while peptides differ in overall abundance across the elution profile multiplicatively, the relative abundance between the mixed samples remains constant across the elution profile. We describe how to apply bi-linear regression models to (18)O stable-isotope labeled data, which allows for the direct comparison of two samples simultaneously. Interpretation of model parameters is also discussed. The incorporation rate of the labeling isotope is estimated as part of the modeling process and can be used as a measure of data quality. Application is demonstrated in a controlled experiment as well as in a complex mixture. RESULTS: Bi-linear regression models allow for more precise and accurate estimates of abundance, in comparison to methods that treat each spectrum independently, by taking into account the abundance of the molecule throughout the entire elution profile, with precision increased by one-to-two orders of magnitude.
解释和量化标记质谱数据很复杂，需要自动化算法，尤其是对于大规模蛋白质组分析。在此，我们建议使用双线性回归在统一模型中量化洗脱曲线中的相对丰度。双线性回归模型利用了这样一个事实，即虽然肽在整个洗脱曲线中的总体丰度呈乘法差异，但混合样品之间的相对丰度在整个洗脱曲线中保持恒定。我们描述了如何将双线性回归模型应用于（18）O稳定同位素标记数据，这允许同时直接比较两个样品。还讨论了模型参数的解释。标记同位素的掺入率作为建模过程的一部分进行估计，可作为数据质量的一种度量。在对照实验以及复杂混合物中展示了其应用。结果：与独立处理每个光谱的方法相比，可以通过考虑整个洗脱曲线中分子的丰度，双线性回归模型能够对丰度进行更精确和准确的估计，精度提高了一到两个数量级。
