Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs.
Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs.
复制标题
DOI：
10.1186/1471-2105-13-s16-s6
复制
发表时间：
2012
影响因子：
3
通讯作者：
Vitek O
中科院分区：
文献类型：
--
作者：
Clough T;Thaminy S;Ragg S;Aebersold R;Vitek O
液相色谱-串联质谱联用（LC-MS/MS）广泛用于定量蛋白质组学研究。这种研究的典型输出是鉴定和定量的肽的列表。然而，生物学和临床兴趣通常集中在蛋白质水平的定量结论上。此外，许多研究通过研究多个相互关联的实验条件来提出复杂的生物学问题。因此，该领域需要通用统计模型来量化蛋白质水平，即使在复杂的研究设计中。我们提出了一个通用的统计建模方法，蛋白质定量在任意复杂的实验设计，如时间过程的研究，或涉及多个实验因素。该方法总结了来自与蛋白质有关的所有特征和所有条件的定量实验信息。它可以实现条件之间的蛋白质显著性分析，以及单个样品或条件中的蛋白质定量。我们实现的方法在一个开源的基于R的软件包MSstats适合研究人员有限的统计和编程背景。我们证明，作为例子，使用两个实验调查与复杂的设计，所有相关的功能和条件的同时统计建模产生更高的灵敏度的蛋白质的显着性分析和更高的准确性的蛋白质定量相比，常用的替代品。该软件可在http://www.stat.purdue.edu/~ovitek/Software.html上获得。
Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs. We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background. We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html.