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dc.contributor.authorPalmer, Daniel Grant
dc.description.abstractRNA-seq data simulated from a negative binomial distribution, sampled without replacement, or modified from read counts were analyzed to compare differential gene expression analysis methods in terms of false discovery rate control and power. The goals of the study were to determine optimal sample sizes/proportions of differential expression needed to adequately control false discovery rate and which differential gene expression methods performed best with the given simulation methods. Parametric tools like edgeR and limma-voom tended to be conservative when controlling false discovery rate from a negative binomial distribution as the proportion of differential expression increased. For the nonparametric simulation methods, many differential gene expression methods did not adequately control false discovery rate and results varied greatly when different reference data sets were used for simulations.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleA Comparative Multiple Simulation Study for Parametric and Nonparametric Methods in the Identification of Differentially Expressed Genesen_US
dc.typeThesisen_US
dc.date.accessioned2022-06-07T20:47:49Z
dc.date.available2022-06-07T20:47:49Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/10365/32715
dc.subjectdifferential expression analysisen_US
dc.subjectfalse discovery rateen_US
dc.subjectpower analysisen_US
dc.subjectRNA-seqen_US
dc.subjectsimulationen_US
dc.identifier.orcid0000-0001-9260-8782
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeScience and Mathematicsen_US
ndsu.departmentStatisticsen_US
ndsu.programStatisticsen_US
ndsu.advisorOrr, Megan


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