Choi Hyungwon, Fermin Damian, Nesvizhskii Alexey I
Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.
Mol Cell Proteomics. 2008 Dec;7(12):2373-85. doi: 10.1074/mcp.M800203-MCP200. Epub 2008 Jul 20.
Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.
光谱计数已成为无标记鸟枪法蛋白质组学中测量蛋白质丰度的常用方法。与此同时，数据分析方法的发展却滞后了。目前，大多数利用光谱计数的研究依赖于简单的数据变换和传统信噪比统计的事后校正。然而，这些调整既无法处理对高丰度蛋白质的偏差，也无法应对由于重复次数有限而产生的缺点。我们提出了一种新颖的统计框架（QSpec），用于差异表达的显著性分析，并扩展到各种实验设计因素以及针对蛋白质特性的调整。使用合成和真实实验数据集，我们表明所提出的方法优于为单个蛋白质搜索差异表达的传统统计方法。我们通过分析一个具有涉及细胞定位和时间进程的复杂实验设计的数据集来说明该模型的灵活性。
