Cho Hyungjun, Smalley David M, Theodorescu Dan, Ley Klaus, Lee Jae K
Department of Statistics, Korea University, Seoul, Korea.
Proteomics. 2007 Oct;7(20):3681-92. doi: 10.1002/pmic.200601034.
LC-MS/MS with certain labeling techniques such as isotope-coded affinity tag (ICAT) enables quantitative analysis of paired protein samples. However, current identification and quantification of differentially expressed peptides (and proteins) are not reliable for large proteomics screening of complex biological samples. The number of replicates is often limited because of the high cost of experiments and the limited supply of samples. Traditionally, a simple fold change cutoff is used, which results in a high rate of false positives. Standard statistical methods such as the two-sample t-test are unreliable and severely underpowered due to high variability in LC-MS/MS data, especially when only a small number of replicates are available. Using an advanced error pooling technique, we propose a novel statistical method that can reliably identify differentially expressed proteins while maintaining a high sensitivity, particularly with a small number of replicates. The proposed method was applied both to an extensive simulation study and a proteomics comparison between microparticles (MPs) generated from platelet (platelet MPs) and MPs isolated from plasma (plasma MPs). In these studies, we show a significant improvement of our statistical analysis in the identification of proteins that are differentially expressed but not detected by other statistical methods. In particular, several important proteins - two peptides for beta-globin and three peptides for von Willebrand Factor (vWF) - were identified with very small false discovery rates (FDRs) by our method, while none was significant when other conventional methods were used. These proteins have been reported with their important roles in microparticles in human blood cells: vWF is a platelet and endothelial cell product that binds to P-selectin, GP1b, and GP IIb/IIIa, and beta-globin is one of the peptides of hemoglobin involved in the transportation of oxygen by red blood cells.
液相色谱-串联质谱联用（LC-MS/MS）结合某些标记技术，如同位素编码亲和标签（ICAT），能够对成对的蛋白质样品进行定量分析。然而，对于复杂生物样品的大规模蛋白质组学筛选，目前差异表达肽（和蛋白质）的鉴定和定量并不可靠。由于实验成本高和样品供应有限，重复次数往往受到限制。传统上，使用简单的倍数变化阈值，这导致假阳性率很高。标准统计方法，如双样本t检验，由于LC-MS/MS数据的高变异性而不可靠且效能严重不足，特别是当只有少量重复样本时。使用先进的误差合并技术，我们提出了一种新颖的统计方法，该方法能够可靠地鉴定差异表达的蛋白质，同时保持高灵敏度，特别是在重复次数较少的情况下。所提出的方法应用于广泛的模拟研究以及血小板产生的微粒（血小板微粒）与血浆分离的微粒（血浆微粒）之间的蛋白质组学比较。在这些研究中，我们表明，在鉴定其他统计方法未检测到的差异表达蛋白质方面，我们的统计分析有显著改进。特别是，我们的方法以非常低的假发现率（FDR）鉴定出了几种重要蛋白质——两条β-珠蛋白肽和三条血管性血友病因子（vWF）肽，而使用其他传统方法时这些蛋白质均无显著性差异。这些蛋白质在人类血细胞微粒中的重要作用已有报道：vWF是一种血小板和内皮细胞产物，可与P-选择素、糖蛋白1b和糖蛋白IIb/IIIa结合，而β-珠蛋白是血红蛋白的肽段之一，参与红细胞运输氧气。
