Year of Publication

2016

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department

Statistics

First Advisor

Dr. Arnold J. Stromberg

Second Advisor

Dr. Chi Wang

Abstract

This paper intend to develop novel statistical methods to improve genomic data analysis, especially for differential analysis. We considered two different data type: NanoString nCounter data and somatic mutation data. For NanoString nCounter data, we develop a novel differential expression detection method. The method considers a generalized linear model of the negative binomial family to characterize count data and allows for multi-factor design. Data normalization is incorporated in the model framework through data normalization parameters, which are estimated from control genes embedded in the nCounter system. For somatic mutation data, we develop beta-binomial model-based approaches to identify highly or lowly mutated genes and to compare somatic mutations between patient groups. An empirical Bayes shrinkage approach is used to improve estimation of model parameters in all projects.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2016.397

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