Methods for Analysis of High Throughput Assay Data

描述(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，这些数据通过对要分析的数据的统计特征的研究而获得信息。指导这一方法开发项目的原则包括：1)高通量化验数据需要转换，以便可以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功能强大的方法，通常足以解决所讨论的真实生物学问题(尽管在需要时应使用非线性模型)；3)统计测试应使用适当的比较标准，以便测试达到与防止过度假阳性一致的最高能力。根据这一建议，我们将继续开发基因表达数据的方法和软件，并将这些方法应用于蛋白质组和代谢组学数据的分析，包括质谱学和核磁共振谱。

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.