University of Illinois Urbana-Champaign

Statistical methods for modeling RNA-Seq short-read data

Dalpiaz, David

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David_Dalpiaz.pdf

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

Description

Title
Statistical methods for modeling RNA-Seq short-read data
Author(s)
Dalpiaz, David
Issue Date
2014-09-16
Director of Research (if dissertation) or Advisor (if thesis)
Ma, Ping
Doctoral Committee Chair(s)
Ma, Ping
Committee Member(s)
  • Douglas, Jeffrey A.
  • Simpson, Douglas G.
  • Zhong, Wenxuan
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2014-09-16T17:25:54Z
Keyword(s)
  • RNA-Seq
  • Gene expression
  • Penalized likelihood
  • Differential expression
Abstract
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.
Graduation Semester
2014-08
Permalink
http://hdl.handle.net/2142/50726
Copyright and License Information
Copyright 2014 David Dalpiaz

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