Protein Quantification in Label-Free LC-MS Experiments
Protein Quantification in Label-Free LC-MS Experiments
复制标题
DOI：
10.1021/pr900610q
复制
发表时间：
2009-11-01
影响因子：
4.4
通讯作者：
Vitek, Olga
中科院分区：
文献类型：
--
作者：
Clough, Timothy;Key, Melissa;Vitek, Olga
许多LC-MS蛋白质组学研究的目标是量化和比较复杂生物混合物中蛋白质的丰度。然而，LC-MS实验的输出不是蛋白质列表，而是量化的光谱特征列表。为了得出蛋白质水平的结论，研究人员通常会应用临时规则，或者采用特征丰度的平均值来获得每个样本的单个蛋白质水平数量。我们认为这两种方法是不够的。我们讨论了两种统计模型，即固定和混合效应方差分析（ANOVA），它将个体特征视为蛋白质丰度的重复测量，并明确说明这种冗余。我们证明，使用穗和临床数据集，所提出的模型提高了检测的灵敏度和特异性，提高了患者特异性蛋白质定量的准确性，并且在存在缺失数据的情况下更加稳健。
The goal of many LC-MS proteomic investigations is to quantify and compare the abundance of proteins in complex biological mixtures However, the output of an LC-MS experiment is not a list of proteins, but a list of quantified spectral features. To make protein-level conclusions, researchers typically apply ad hoc rules, or take an average of feature abundance to obtain a single protein-level quantity for each sample We argue that these two approaches are inadequate. We discuss two statistical models, namely, fixed and mixed effects Analysis of Variance (ANOVA), which views individual features as replicate measurements of a protein's abundance, and explicitly account for this redundancy. We demonstrate, using a spike-in and a clinical data set, that the proposed models improve the sensitivity and specificity of testing, improve the accuracy of patient-specific protein quantifications, and are more robust in the presence of missing data.