2026-09-21T07:45:01Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/50726 2023-07-11 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Ma, Ping Ma, Ping Douglas, Jeffrey A. Simpson, Douglas G. Zhong, Wenxuan Dalpiaz, David 2014-09-16T17:25:54Z 2014-09-16T17:25:54Z 2014-08 2014-09-16 2014-08 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. Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-06-30T18:41:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Dalpiaz_David.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5) Made available in DSpace on 2014-09-16T17:25:54Z (GMT). No. of bitstreams: 2 David_Dalpiaz.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5) license.txt: 4063 bytes, checksum: 4b5b2be68d678e4cc7d7d7288aabc005 (MD5) http://hdl.handle.net/2142/50726 en Copyright 2014 David Dalpiaz RNA-Seq Gene expression Penalized likelihood Differential expression Statistical methods for modeling RNA-Seq short-read data text Statistics 1583 Statistics 0329 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Statistics -UIUC 10KS0329PHD