Statistical methods for modeling RNA-Seq short-read data

Dalpiaz, David

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

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

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

Copyright and License Information

Copyright 2014 David Dalpiaz

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