Analysis of factorial time course microarray data with applications to a clinical study of burn injury

Many microarray experiments have factorial designs. But there are few statistical methods developed explicitly to handle the factorial analysis in these experiments. In the first part of the dissertation, we propose a bootstrap-based non-parametric ANOVA (NANOVA) method and a gene classification algorithm to classify genes into different groups according to the factor effects. The proposed method encompasses one-way and two-way models, as well as balanced and unbalanced experimental designs. False discovery rate (FDR) estimation is embedded into the procedure, and the method is robust to outliers. The gene classification algorithm is based on a series of NANOVA tests. FDR of each test is carefully controlled. The gene expression pattern in each group is modeled by a different ANOVA structure. We demonstrate the performance of NANOVA using simulated and real microarray data sets.;In the second part of the dissertation, we develop a method for the analysis for factorial time course microarray data based on NANOVA. Time-course microarray experiment is capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We develop a method that evaluates factor effects by pooling information across time while accounting for multiple testing and non-normality of microarray data. The method can extract gene specific response features and model their dependency on the experimental factors. Both longitudinal and cross-sectional time course data can be handled by our approach. The method is used to analyze microarray data from a large-scale clinical study on burn injury. The analysis identified many genes responsive to burn injury, including those with responses that are age-specific and gender-specific. We also compared different tissue responses in pediatric and adult burn patients and analyzed the age impact on survivability of adult burn patients.