Spectral index for assessment of differential protein expression in shotgun proteomics
Spectral index for assessment of differential protein expression in shotgun proteomics
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DOI：
10.1021/pr070271
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发表时间：
2008-03-01
影响因子：
4.4
通讯作者：
Heinecke, Jay W.
中科院分区：
文献类型：
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作者：
Fu, Xiaoyun;Gharib, Sina A.;Heinecke, Jay W.
检测差异表达的蛋白质是蛋白质组学的一个关键目标。我们描述了一种无标记方法，即光谱指数，用于通过鸟枪蛋白质组学分析源自生物样本的大规模数据集中的相对蛋白质丰度。光谱指数由两个生化合理特征组成：相对蛋白质丰度（通过光谱计数评估）和一组内具有可检测肽的样品数量。我们将光谱指数与排列分析相结合，建立置信区间，用于评估囊性纤维化和对照受试者的支气管肺泡灌洗液中的差异蛋白表达。由光谱指数确定的蛋白质丰度的显着差异与独立的生化测量结果非常吻合。当用于分析模拟数据集时，光谱指数通过正确识别最大数量的差异表达蛋白质，优于其他四种统计检验（学生 t 检验、G 检验、贝叶斯 t 检验和微阵列显着性分析）。对应分析和功能注释分析表明，光谱索引提高了与临床表型相对应的富集蛋白质的识别。光谱指数易于实现且具有统计稳健性，并且其结果易于以图形方式解释。因此，它对于生物标志物的发现以及正常和疾病状态之间蛋白质表达的比较应该是有用的。
Detecting differentially expressed proteins is a key goal of proteomics. We describe a label-free method, the spectral index, for analyzing relative protein abundance in large-scale data sets derived from biological samples by shotgun proteomics. The spectral index is comprised of two biochemically plausible features: relative protein abundance (assessed by spectral counts) and the number of samples within a group with detectable peptides. We combined the spectral index with permutation analysis to establish confidence intervals for assessing differential protein expression in bronchoalveolar lavage fluid from cystic fibrosis and control subjects. Significant differences in protein abundance determined by the spectral index agreed well with independent biochemical measurements. When used to analyze simulated data sets, the spectral index outperformed four other statistical tests (Student's t-test, G-test, Bayesian t-test, and Significance Analysis of Microarrays) by correctly identifying the largest number of differentially expressed proteins. Correspondence analysis and functional annotation analysis indicated that the spectral index improves the identification of enriched proteins corresponding to clinical phenotypes. The spectral index is easily implemented and statistically robust, and its results are readily interpreted graphically. Therefore, it should be useful for biomarker discovery and comparisons of protein expression between normal and disease states.