Statistical methods and software for high-throughput gene expression experiments
by Bullard, James Hudson, Ph.D., UNIVERSITY OF CALIFORNIA, BERKELEY, 2009, 184 pages; 3411141

Abstract:

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.

 
AdviserSandrine Dudoit
SchoolUNIVERSITY OF CALIFORNIA, BERKELEY
SourceDAI/B 71-06, p. , Jul 2010
Source TypeDissertation
SubjectsBiostatistics; Bioinformatics
Publication Number3411141
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