Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics
Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics
复制标题
DOI：
10.1021/pr050300l
复制
发表时间：
2006-02-01
影响因子：
4.4
通讯作者：
Lipton, MS
中科院分区：
文献类型：
--
作者：
Callister, SJ;Barry, RC;Lipton, MS
研究了集中趋势、线性回归、局部加权回归和分位数技术对高通量液相色谱-傅里叶变换离子回旋共振质谱（LC-FTICR MS）获得的肽丰度测量结果进行归一化处理。从三个样品组中获得任意丰度的肽，包括一个标准蛋白质样品，两个来自不同生长阶段的耐辐射球菌样品，以及两个来自对照和甲基苯丙胺应激小鼠（菌株C57BL/6）的小鼠纹状体样品。通过估计标准化前后的外来变异性，对所选择的标准化技术在没有和存在生物变异性的情况下进行评估。在归一化之前，观察到每个样本集的重复运行具有统计学差异，而在归一化之后，重复运行不再具有统计学差异。虽然所有技术都在一定程度上减少了系统偏差，但分配给技术的排名显示，对于大多数LC-FTICR-MS分析，线性回归归一化排名第一或第二。然而，这些技术之间缺乏明确的趋势，这表明需要进一步研究适应无标记蛋白质组学的规范化方法。然而，这项研究是评估解决与相对量化和无标记蛋白质组学相关的系统性偏差的方法的重要一步。
Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.