Methods for Analysis of High Throughput Assay Data

高通量检测数据的分析方法

基本信息

批准号：
7392384
负责人：
David M. Rocke
金额：
$ 21.43万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-06-01 至 2011-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7392384
关键词：
Address Agreement Algorithms Analysis of Variance Area Biological Biological Markers Characteristics Class Classification Code Color Communities Complex Complex Mixtures Computer software Data Data Set Development Dose Dyes Empirical Research Environment Experimental Designs Family Gene Expression Genes Goals Height Language Linear Models Linux Mass Spectrum Analysis Measurement Measures Metabolic Methodology Methods Modeling Multivariate Analysis NMR Spectroscopy Non-linear Models Numbers Oligonucleotides Phase Process Production Proteomics Range Rate Reaction Time Research Research Personnel Running Score Shipping Ships Solutions Speed Standards of Weights and Measures Statistical Models Surface System Techniques Testing Time Validation Variant Work Writing base compare effectiveness density experience heuristics high throughput screening improved metabolomics method development open source prevent response software development user friendly software

项目摘要

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.

描述（由申请人提供）：该项目的主要目标是开发用于基因表达数据，蛋白质组学数据和代谢组学数据的分析和软件实现方法，这些数据是通过研究要分析数据的统计特征来告知的。指导该方法开发项目的原理包括以下内容：1）高通量测定数据需要转换，以便可以在整个范围内以相同的方式进行测量； 2）线性统计模型提供了一类强大的方法，这些方法通常足以解决所讨论的实际生物学问题（尽管需要在需要时可用非线性模型）； 3）统计测试应使用适当的比较标准，以使测试达到与防止过度假阳性相一致的最高功率。在此提案下，我们将继续开发用于基因表达数据的方法和软件，并通过质谱和NMR光谱法对蛋白质组学和代谢组学数据进行分析的方法进行了处理。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Genomic characterization of a three-dimensional skin model following exposure to ionizing radiation.

DOI：
10.1093/jrr/rrs063
发表时间：
2012-11-01
期刊：
Journal of radiation research
影响因子：
2
作者：
Yunis R;Albrecht H;Kalanetra KM;Wu S;Rocke DM
通讯作者：
Rocke DM

Gene regulation in parthenocarpic tomato fruit.

DOI：
10.1093/jxb/erp227
发表时间：
2009
期刊：
Journal of experimental botany
影响因子：
6.9
作者：
Martinelli F;Uratsu SL;Reagan RL;Chen Y;Tricoli D;Fiehn O;Rocke DM;Gasser CS;Dandekar AM
通讯作者：
Dandekar AM

Differential network analyses of Alzheimer's disease identify early events in Alzheimer's disease pathology.