Jung Klaus
Department of Medical Statistics, Georg-August-University Göttingen, Göttingen, Germany.
Methods Mol Biol. 2011;696:259-72. doi: 10.1007/978-1-60761-987-1_16.
High-throughput experiments in proteomics, such as 2-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS), yield usually high-dimensional data sets of expression values for hundreds or thousands of proteins which are, however, observed on only a relatively small number of biological samples. Statistical methods for the planning and analysis of experiments are important to avoid false conclusions and to receive tenable results. In this chapter, the most frequent experimental designs for proteomics experiments are illustrated. In particular, focus is put on studies for the detection of differentially regulated proteins. Furthermore, issues of sample size planning, statistical analysis of expression levels as well as methods for data preprocessing are covered.
蛋白质组学中的高通量实验，如二维凝胶电泳（2-DE）和质谱分析（MS），通常会产生包含数百或数千种蛋白质表达值的高维数据集，然而这些数据仅在相对较少的生物样本上观测得到。用于实验规划和分析的统计方法对于避免错误结论和获得可靠结果至关重要。在本章中，将阐述蛋白质组学实验中最常见的实验设计。特别关注用于检测差异调节蛋白质的研究。此外，还涵盖了样本量规划、表达水平的统计分析以及数据预处理方法等问题。
