质谱数据中蛋白质差异表达和峰选择的二元判别分析
Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis
摘要：
动机：蛋白质组质谱分析在临床诊断中正变得常规，例如使用血液样本监测癌症生物标志物。然而，差异蛋白质组学和鉴定相关的类分离的峰仍然具有挑战性。结果：在这里，我们介绍了一种简单而有效的方法，用于识别差异表达的蛋白质，使用二元判别分析。该方法通过蛋白质表达值的数据自适应阈值化和随后使用相对熵度量对二分特征进行排名来工作。我们的框架可以被视为Tibshirani等人（2004年）的“峰值概率对比”方法的推广，可以应用于两组和多组环境。我们的方法是计算成本低，并显示在一个大规模的药物发现测试数据集的分析相当于一个随机森林的预测精度。此外，我们能够在来自胰腺癌研究的质谱数据的分析中鉴定出在原始研究中未识别的生物学相关和统计学预测标记物峰。供货情况：二进制判别分析的方法在R包binda中实现，该包在GNU通用公共许可证（版本3或更高版本）下可从CRAN在URL www.example.com免费获得http://cran.r-project.org/web/packages/binda/。复制所有描述的分析的R脚本可从网页http://strimmerlab.org/software/binda/获得。
英文摘要：
Motivation: Proteomic mass spectrometry analysis is becoming routine in clinical diagnostics, for example to monitor cancer biomarkers using blood samples. However, differential proteomics and identification of peaks relevant for class separation remains challenging. Results: Here, we introduce a simple yet effective approach for identifying differentially expressed proteins using binary discriminant analysis. This approach works by data-adaptive thresholding of protein expression values and subsequent ranking of the dichotomized features using a relative entropy measure. Our framework may be viewed as a generalization of the `peak probability contrast' approach of Tibshirani et al. (2004) and can be applied both in the two-group and the multi-group setting. Our approach is computationally inexpensive and shows in the analysis of a large-scale drug discovery test data set equivalent prediction accuracy as a random forest. Furthermore, we were able to identify in the analysis of mass spectrometry data from a pancreas cancer study biological relevant and statistically predictive marker peaks unrecognized in the original study. Availability: The methodology for binary discriminant analysis is implemented in the R package binda, which is freely available under the GNU General Public License (version 3 or later) from CRAN at URL http://cran.r-project.org/web/packages/binda/ . R scripts reproducing all described analyzes are available from the web page http://strimmerlab.org/software/binda/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
