Experimental Null Method to Guide the Development of Technical Procedures and to Control False-Positive Discovery in Quantitative Proteomics.
Experimental Null Method to Guide the Development of Technical Procedures and to Control False-Positive Discovery in Quantitative Proteomics.
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DOI：
10.1021/acs.jproteome.5b00200
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发表时间：
2015-10-02
影响因子：
4.4
通讯作者：
Qu J
中科院分区：
文献类型：
--
作者：
Shen X;Hu Q;Li J;Wang J;Qu J
全面准确地评估数据质量和假阳性生物标志物发现对于指导定量蛋白质组学的方法开发/优化至关重要，但由于蛋白质组学数据的高度复杂性和独特性，这仍然具有挑战性。在这里，我们描述了一个实验性的空（EN）方法来解决这一需求。由于该方法使用与病例对照实验相同的蛋白质组学样品、相同的程序和相同的批次来实验性地测量零分布（技术或生物重复），因此它正确地反映了技术可变性的集体效应（例如，样品制备、LC-MS分析和数据处理中的变化/偏差）和项目特定特征（例如，蛋白质组和生物变异的特征）对定量分析性能的影响。为了证明概念，我们采用EN方法来评估定量准确性和精密度，以及使用不同的实验和数据处理方法以及在各种细胞和组织蛋白质组中量化组之间细微比例变化的能力。结果发现，定量特征，样本量，实验设计，数据处理策略和色谱分离质量的选择可以深刻地影响定量精密度和准确性的无标记定量。EN方法也被证明是一个实用的工具，以确定最佳的实验参数和合理的比率截止值，用于在特定的蛋白质组学实验中进行可靠的蛋白质定量，例如，以确定每组提供足够的发现能力的必要数量的技术/生物学重复。此外，我们评估了EN方法在发现改变的蛋白质中估计假阳性水平的能力，使用两个调制的样品集分别使用技术和生物学重复模拟蛋白质组学分析，其中真阳性/阴性是已知的，并且跨越很宽的浓度范围。观察到EN方法正确地反映了蛋白质组学系统中的零分布，并准确地测量了错误改变的蛋白质发现率（FADR）。总之，EN方法提供了一种简单，实用，准确的替代方法，以生物学为基础的方法，用于蛋白质组学实验的开发和评估，并可普遍适用于各种类型的定量技术。
Comprehensive and accurate evaluation of data quality and false-positive biomarker discovery is critical to direct the method development/optimization for quantitative proteomics, which nonetheless remains challenging largely due to the high complexity and unique features of proteomic data. Here we describe an experimental null (EN) method to address this need. Because the method experimentally measures the null distribution (either technical or biological replicates) using the same proteomic samples, the same procedures and the same batch as the case-vs-contol experiment, it correctly reflects the collective effects of technical variability (e.g., variation/bias in sample preparation, LC–MS analysis, and data processing) and project-specific features (e.g., characteristics of the proteome and biological variation) on the performances of quantitative analysis. To show a proof of concept, we employed the EN method to assess the quantitative accuracy and precision and the ability to quantify subtle ratio changes between groups using different experimental and data-processing approaches and in various cellular and tissue proteomes. It was found that choices of quantitative features, sample size, experimental design, data-processing strategies, and quality of chromatographic separation can profoundly affect quantitative precision and accuracy of label-free quantification. The EN method was also demonstrated as a practical tool to determine the optimal experimental parameters and rational ratio cutoff for reliable protein quantification in specific proteomic experiments, for example, to identify the necessary number of technical/biological replicates per group that affords sufficient power for discovery. Furthermore, we assessed the ability of EN method to estimate levels of false-positives in the discovery of altered proteins, using two concocted sample sets mimicking proteomic profiling using technical and biological replicates, respectively, where the true-positives/negatives are known and span a wide concentration range. It was observed that the EN method correctly reflects the null distribution in a proteomic system and accurately measures false altered proteins discovery rate (FADR). In summary, the EN method provides a straightforward, practical, and accurate alternative to statistics-based approaches for the development and evaluation of proteomic experiments and can be universally adapted to various types of quantitative techniques.
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影响因子：
3.4
作者：
Searle, Brian C.
通讯作者：
Searle, Brian C.
影响因子：
7
作者：
Karp, Natasha A.;McCormick, Paul S.;Lilley, Kathryn S.
通讯作者：
Lilley, Kathryn S.
影响因子：
2.3
作者：
Bertolino, Francesco;Cabras, Stefano;Racugno, Walter
通讯作者：
Racugno, Walter
影响因子：
4.4
作者：
Duan X;Young R;Straubinger RM;Page B;Cao J;Wang H;Yu H;Canty JM;Qu J
通讯作者：
Qu J
影响因子：
3.7
作者：
Margolin, Adam A.;Ong, Shao-En;Golub, Todd R.
通讯作者：
Golub, Todd R.