Methods for Analysis of High Throughput Assay Data

项目描述（由申请人提供）：该项目的主要目标是开发基因表达数据、蛋白质组学数据和代谢组学数据的分析方法和软件实现，这些数据是通过对待分析数据的统计特征的研究而获得的。指导该方法开发项目的原则包括以下内容：1)需要对高通量分析数据进行转换，以便在整个范围内以相同的方式处理测量；2)线性统计模型提供了一类强大的方法，通常足以解决实际的生物学问题（尽管非线性模型应该在需要时可用）；3)统计检验应采用适当的比较标准，使检验达到与防止过多误报一致的最高功效。根据该提案，我们将继续开发基因表达数据的方法和软件，并将这些方法延续到通过质谱和核磁共振光谱分析蛋白质组学和代谢组学数据。

DESCRIPTION (provided by applicant): The main goal of this project is to develop methods of analysis and software implementations for gene expression data, proteomics data, and metabolomics data that are informed by research on the statistical characteristics of the data that are to be analyzed. The principles guiding this methods-development project include the following: 1) high-throughput assay data require transformation so that measurements can be treated in the same fashion across the full range; 2) linear statistical models provide a powerful class of methods that are often sufficient to address the real biological problems at issue (though nonlinear models should be available when required); 3) statistical tests should use appropriate comparison standards so that the tests achieve the highest power consistent with preventing excessive false positives. Under this proposal, we will continue the development of methods and software for gene expression data, and carry over the methods to the analysis of proteomics and metabolomics data by mass spectrometry and NMR spectroscopy.