Li Qingbo
Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60607, Department of Microbiology and Immunology, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612.
Int J Proteomics. 2010 Jan 1;2010. doi: 10.1155/2010/731582.
Selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change when sample replicates are limited. This study presents a label-free quantitative proteomics scheme that was used to select differentially regulated proteins with single-peptide hits and with <2-fold change at a 5% false discovery rate. The scheme incorporated a labeled internal control into two unlabeled samples to facilitate error modeling when there were no replicates for the unlabeled samples. The results showed that, while both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold could be used, neither of them alone was stringent enough to select differentially regulated proteins at a 5% false discovery rate. Thus, the rule of minimum number of permuted significant pairings (MPSP) was introduced to reduce false discovery rates in combination with PLGEM-STN or a fold-change threshold. MPSP played a critical role in extending the selection of differentially regulated proteins to those with single-peptide hits or with lower fold-changes. Although the approaches were demonstrated for limited sample replicates, they should also be applicable to the situation where more sample replicates are available.
对于那些只有单肽命中或倍数变化较小的蛋白质，在样本重复有限的情况下，选择具有统计学意义的差异调节蛋白仍然很困难。本研究提出了一种无标记定量蛋白质组学方案，用于选择具有单肽命中且倍数变化小于2倍、错误发现率为5%的差异调节蛋白。该方案将标记的内部对照纳入两个未标记的样本中，以便在未标记样本没有重复时便于误差建模。结果表明，虽然可以使用具有信噪比统计量的幂律全局误差模型（PLGEM-STN）和恒定的倍数变化阈值，但单独使用它们中的任何一个都不够严格，无法在5%的错误发现率下选择差异调节蛋白。因此，引入了最小置换显著配对数（MPSP）规则，与PLGEM-STN或倍数变化阈值相结合以降低错误发现率。MPSP在将差异调节蛋白的选择扩展到具有单肽命中或较低倍数变化的蛋白方面发挥了关键作用。尽管这些方法是针对有限的样本重复进行验证的，但它们也应该适用于有更多样本重复的情况。
