Biometric tools for microarrays

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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.