Statistical methods for modeling RNA-Seq short-read data

This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Dalpiaz, David

Contributors dc:contributor

• Ma, Ping
• Douglas, Jeffrey A.
• Simpson, Douglas G.
• Zhong, Wenxuan

• RNA-Seq
• Gene expression
• Penalized likelihood
• Differential expression

Statement dc:rights

• Copyright 2014 David Dalpiaz

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/50726

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/50726