Statistics in Experimental Design, Preprocessing, and Analysis of Proteomics Data
Statistics in Experimental Design, Preprocessing, and Analysis of Proteomics Data
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DOI：
10.1007/978-1-60761-987-1_16
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发表时间：
2011-01-01
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影响因子：
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通讯作者：
Jung, Klaus
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文献类型：
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作者：
Jung, Klaus
蛋白质组学中的高通量实验，例如二维凝胶电泳 (2-DE) 和质谱 (MS)，通常会产生数百或数千种蛋白质表达值的高维数据集，然而，这些蛋白质仅在相对少量的生物样品中观察到。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的结果非常重要。本章阐述了蛋白质组学实验中最常见的实验设计。特别是，重点放在检测差异调节蛋白的研究上。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的问题。
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 arc, 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.