Development and Evaluation of Normalization Methods for Label-free Relative Quantification of Endogenous Peptides
Development and Evaluation of Normalization Methods for Label-free Relative Quantification of Endogenous Peptides
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DOI：
10.1074/mcp.m800514-mcp200
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发表时间：
2009-10-01
影响因子：
7
通讯作者：
Andren, Per E.
中科院分区：
文献类型：
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作者：
Kultima, Kim;Nilsson, Anna;Andren, Per E.
评价了10种不同的归一化方法在无标记纳米LC-MS内源性脑多肽数据上的性能。研究中使用了来自三个不同物种(小鼠、大鼠和日本鹌鹑)的数据集，每个数据集由35-45个单独的LC-MS分析组成。每个样本集包含技术和生物重复，LC-MS分析以随机区组方式进行。所有三个数据集中的多肽都显示出与LC-MS分析顺序相关的偏倚。全局归一化方法只能在一定程度上纠正这种偏差。只有新的归一化程序RegrRun(线性回归后分析顺序归一化)纠正了这种类型的偏差。RegrRun程序在测试的归一化方法中表现最好，与原始数据相比，中位数S.D.平均降低了43%。这种方法还产生了区组间差异最小的多肽，而在所有三个数据集中的处理组之间产生了最大比例的差异表达峰。线性回归归一化(Regr)的表现次之，与原始数据相比，中位数标准差平均降低了38%。与原始数据相比，所有其他检查方法平均降低了20-30%的中位数标准偏差。《分子与细胞蛋白质组学》8：2285-2295,2009。
The performances of 10 different normalization methods on data of endogenous brain peptides produced with label- free nano-LC-MS were evaluated. Data sets originating from three different species (mouse, rat, and Japanese quail), each consisting of 35-45 individual LC-MS analyses, were used in the study. Each sample set contained both technical and biological replicates, and the LC-MS analyses were performed in a randomized block fashion. Peptides in all three data sets were found to display LC-MS analysis order-dependent bias. Global normalization methods will only to some extent correct this type of bias. Only the novel normalization procedure RegrRun (linear regression followed by analysis order normalization) corrected for this type of bias. The RegrRun procedure performed the best of the normalization methods tested and decreased the median S. D. by 43% on average compared with raw data. This method also produced the smallest fraction of peptides with interblock differences while producing the largest fraction of differentially expressed peaks between treatment groups in all three data sets. Linear regression normalization (Regr) performed second best and decreased median S.D. by 38% on average compared with raw data. All other examined methods reduced median S. D. by 20-30% on average compared with raw data. Molecular & Cellular Proteomics 8: 2285-2295, 2009.