Statistical Analysis of Gene Expression Profile: Transcription Network Inference and Sample Classification

The copious information generated from transcriptomes gives us an opportunity to learn biological processes as integrated systems; however, due to numerous sources of variation, high dimensions of data structure, various levels of data quality, and different formats of the inputs, dissecting and interpreting such data presents daunting challenges to scientists. The goal of this research is to provide improved and new statistical tools for analyzing transcriptomes data to identify gene expression patterns for classifying samples, to discover regulatory gene networks using natural genetic perturbations, to develop statistical methods for model fitting and comparison of biochemical networks, and eventually to advance our capability to understand the principles of biological processes at the system level. Acknowledgements I would like to thank my advisor, Dr. Ina Hoeschele, for accepting me and introducing me to the field of Statistical Genomics and Bioinformatics. I have benefited a lot from her permission to let me freely choose courses in my first two years of graduate study, her rigorous training on statistics, her willingness to let me freely develop ideas, an