Date Available
10-6-2016
Year of Publication
2016
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Arts and Sciences
Department/School/Program
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
Recommended Citation
Wang, Hong, "IMPROVED MODELS FOR DIFFERENTIAL ANALYSIS FOR GENOMIC DATA" (2016). Theses and Dissertations--Statistics. 21.
https://uknowledge.uky.edu/statistics_etds/21
