Core--Statistical Analysis of Toxics Measurement Data

核心--毒物测量数据统计分析

• 批准号: 6900561
• 负责人: David M. Rocke
• 金额: $ 5.59万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6900561
• 关键词: animal data; bioinformatics; biomedical facility; environmental toxicology; human data; statistics /biometry

This project has two purposes. As the statistics and bioinformatics core, the project will provide statistical and database support to other superfund projects. This will typically involve assistance in experimental design, in the analysis and interpretation of data, in the proposal and development of appropriate statistical methods of data analysis, and in the storage and analysis of microarray, mass spectrometry, and NMR spectroscopy data. In addition, this project proposes the development and refinement of statistical methods and algorithms for the analysis of toxics measurement data, gene expression data, proteomics data, and for the creation of new and improved measurement techniques. A particular emphasis is on the development of new statistical methods for design and analysis of experiments using microarrays (including immunoarrays, DNA arrays, and oligonucleotide arrays), mass spectrometry, and NMR spectroscopy, as well as assisting with the bioinformatics needs associated with these data-intensive methods. We will help deal with such problems as background and baseline correction, peak alignment, compound identification, nonlinear calibration, nonconstant variance, outliers, values near or below detection limits, and high-dimensional and large data sets. Many analytical methods can be made more efficient and effective by careful statistical design and analysis of the data. This may be important to human health, since it allows more frequent monitoring of hazardous sites or for human biomarkers of exposure for the same cost, and since it aids in the development and use of analytical techniques to detect toxic substances both clinically and in the field at lower levels and with greater accuracy than existing methods.

该项目有两个目的。作为统计和生物信息学核心，该项目将为其他超级基金项目提供统计和数据库支持。这通常涉及实验设计，数据分析和解释，适当的数据分析统计方法以及微阵列，质谱和NMR光谱数据的存储和分析中的帮助和开发。此外，这个项目 提出了统计方法和算法的开发和完善，以分析有毒物质测量数据，基因表达数据，蛋白质组学数据以及创建新的和改进的测量技术。特别重点是开发使用微阵列（包括免疫阵列，DNA阵列和寡核苷酸阵列），质谱和NMR的新型统计方法设计和分析实验的统计方法。 光谱法以及协助与这些数据密集型方法相关的生物信息学需求。我们将帮助解决诸如背景和基线校正，峰对齐，复合识别，非线性校准，非稳定方差，离群值，接近或低于检测限的值以及高维和大数据集的问题。通过仔细的统计设计和 数据分析。这可能对人类健康很重要，因为它可以更频繁地监测危险部位或以相同成本以相同成本的人类暴露量监测，并且由于它有助于开发和使用分析技术，以在临床和野外以较低的水平和比现有方法更高的水平检测有毒物质。