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.

产品说明： 该训练方案的目标是设计、实现和验证S-评分算法的混合效应模型扩展（Zhang et al.，J Mol Biol，2001）用于寡核苷酸微阵列。最初开发S评分是为了提供现有软件的替代方案，用于测量差异基因表达。它基于误差模型，其中检测到的信号与高表达基因的探针对信号成比例，但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，该相对变化将探针信号转换为具有均衡误差的多个测量值，这些测量值被求和以形成S分数。验证研究证实，S评分优于许多其他方法。然而，通过将其扩展到能够处理两个以上样本和预测变量中的混合效应的更一般的模型，可以实现对S分数的改进。混合效应模型的使用更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群体的子集。这样的模型可以更准确地捕捉微阵列实验的相关结构，并在检测基因表达变化方面提供更大的能力。在他的指导下，PI将开发一个混合效应模型扩展和相应的R语言软件算法。这将导致一个软件算法的创建和广泛传播，该算法结合了基因表达分析的最新创新。