Mixed Effects Modeling of Microarrays Using the S-score

描述：本训练方案的目标是为寡核苷酸微阵列设计、实现和验证S-score算法的混合效应模型扩展（Zhang et al., J Mol Biol, 2001）。S-score最初是为了替代现有的测量差异基因表达的软件而开发的。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信号成正比，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，该变化将探针信号转换为具有相等误差的多个测量值，并将其求和形成s分数。验证研究证实，S-score优于许多其他方法。然而，通过将s分数扩展到能够处理两个以上样本和预测变量混合效应的更一般的模型，可以实现s分数的改进。混合效应模型的使用更接近地描述了微阵列研究，其中某些因素代表了被研究人群的一个子集。该模型更准确地捕捉了微阵列实验的相关结构，为检测基因表达变化提供了更大的能力。在导师的指导下，PI将用R语言开发混合效果模型扩展和相应的软件算法。这将导致结合基因表达分析最新创新的软件算法的创建和广泛分发。

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.