Mixed Effects Modeling of Microarrays Using the S-score

使用 S 分数对微阵列进行混合效应建模

• 批准号: 7272023
• 负责人: RICHARD E KENNEDY
• 金额: $ 2.78万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7272023
• 关键词: Algorithms; Automobile Driving; Brain region; Code; Computer software; Environment; Gene Expression; Genes; Goals; Language; Lead; Measurement; Measures; Mentors; Methods; Modeling; Oligonucleotide Microarrays; Population; Relative (related person); Sampling; Score; Sensitivity and Specificity; Signal Transduction; Software Tools; Structure; Sum; Techniques; Tissue-Specific Gene Expression; Training; base; design; innovation; open source; programs; research study; validation studies

DESCRIPTION: The goal of this training proposal is to design, implement, and validate a mixed effects model extension of the S-score algorithm (Zhang et al., J Mol Biol, 2001) for oligonucleotide microarrays. The S-score was originally developed to provide alternatives to existing software for measuring differential gene expression. It is based on an error model in which the detected signal is proportional to the probe pair signal for highly expressed genes, but approaches a background level (rather than 0) for low levels of expression. This model is used to calculate a relative change in probe pair intensities that converts probe signals into multiple measurements with equalized errors, which are summed to form the S-score. Validation studies confirmed that the S-score outperformed many other methods. However, improvements on the S-score may be realized by extending it to a more general model capable of handling more than two samples and mixed effects in the predictor variables. The use of a mixed effects model more closely describes microarray studies, where certain factors represent a subset of the population being studied. Such a model captures the correlation structure of microarray experiments more accurately and offers greater power in detecting gene expression changes. Under his mentors, the PI will develop a mixed effects model extension and corresponding software algorithms in the R language. This will lead to the creation and widespread distribution of a software algorithm incorporating the latest innovations in gene expression analysis.

描述：该培训建议的目的是为寡核苷酸微阵列设计，实施和验证S分数算法的混合效应模型扩展（Zhang等，J Mol Biol，2001）。 S分数最初是为了为测量差异基因表达的现有软件提供替代方案。它基于一个误差模型，其中检测到的信号与高表达基因的探针对成正比，但是对于低表达水平，接近背景水平（而不是0）。该模型用于计算探针对强度的相对变化，该强度将探针信号转换为具有均衡误差的多个测量值，这将求和以形成S分数。验证研究证实，S分数的表现超过了许多其他方法。但是，可以通过将其扩展到能够处理两个以上样品和预测变量中的混合效果的更通用模型来实现S分数的改进。混合效应模型的使用更加紧密地描述了微阵列研究，其中某些因素代表了正在研究的人群的一个子集。这种模型更准确地捕获了微阵列实验的相关结构，并在检测基因表达变化方面具有更大的功能。在他的指导下，PI将使用R语言开发混合效应模型扩展和相应的软件算法。这将导致结合基因表达分析中最新创新的软件算法的创建和广泛分布。