Biometric tools for microarrays

微阵列的基因表达数据为理解基因作用，特别是杂种优势提供了广泛的选择。通常，在微阵列上点样1000个基因并筛选差异表达，而由于预算限制，重复的数量通常是有限的，因此变量的数量远远超过重复的数量，过度拟合的危险甚至比传统的QTL作图更大。此外，表达数据受到无数随机变化来源的影响，从而模糊了表达的基本模式。鉴于这些困难，考虑到所有变异来源的现实建模至关重要。该项目将为这项任务开发一个通用的混合建模和模型选择框架。分析早期阶段的一个关键问题是数据转换的选择。虽然对数转换由于其对倍数变化的简单解释而经常使用，但也有人提出了其他方法。这个项目将探索不同转换的属性和适用性。此外，将包括乘法和因子分析模型，以解释不同基因之间的尺度差异。将特别强调说明误差变化的所有相关来源（染料、载玻片、行和列、重复、田间和实验室试验变化等）。最后，解释和预测杂种优势的模型将被整合到总体建模框架中。

Gene-expression data from microarrays open up a wide range of options for understanding gene action in general and heterosis in particular. Typically, many 1.000 genes are spotted on a microarray and screened for differential expression, while the number of replicates is often limited due to budget constraints, so the number of variables by far exceeds that of replications and the danger of over-fitting is even larger than in conventional QTL mapping. Also, expression data are influenced by a myriad of sources of random variation, thus obscuring the underlying pattern of expression. In light of these difficulties, realistic modelling that takes into account all sources of variation is of paramount importance. This project will develop a general mixed modelling and model selection framework for this task. A crucial question in the early stages of analysis is the choice of data transformation. While the log-transformation has been frequently used because of its simple interpretation with respect to fold change, others have been suggested. This project will explore the properties and suitability of different transformations. In addition multiplicative and factor-analytic models will be included to account for scale differences among different genes. Particular emphasis will be given to accounting for all relevant sources of error variation (dyes, slides, rows an columns, replications, field and laboratory trial variation, etc.). Finally, models for explaining and predicting heterosis will be integrated into the general modelling framework.