Statistical methods and software for high-throughput gene expression experiments

I propose statistical methods and software for the analysis of high-throughput gene expression experiments. Specifically, I investigate basic properties of mRNA-Seq data obtained from a Genome Analyzer benchmarking experiment. Differential expression (DE) is inferred using various statistical tests and results are compared to gold-standard qRT-PCR and Affymetrix microarray data. Normalization methods are evaluated in context of sensitivity/specificity trade-offs for DE. Additionally, I propose tests of allele-specific DE in an inter-species yeast hybrid. DE calls are then used to infer gene-networks which have undergone selection within the species-specific lineages. Finally, I present statistical software for the visualization and analysis of high-throughput gene expression data.