Date of Award

2018

Publisher

North Dakota State University

Document Type

Thesis

Degree Awarded

Master of Science (MS)

Program

Statistics

Department

Statistics

College

Science and Mathematics

Faculty Advisor

Orr, Megan

Description

Background: The DECODE method integrates differential co-expression and differential expression analysis methods to better understand biological functions of genes and their associations with disease. The DECODE method originally was designed to analyze large sample gene expression experiments, however most gene expression experiments consist of small sample sizes. This paper proposes modified test statistic to replace the traditional test statistic in the DECODE method. Using three simulations studies, we compare the performances of the modified and traditional DECODE methods using measures of sensitivity, positive predictive value (PPV), false discovery rate (FDR), and overall error rate for genes found to be highly differentially expressed and highly differentially co-expressed. Results: In comparison of sensitivity and PPV a minor increase is seen when using modified DECODE method along with minor decrease in FDR and overall error rate. Thus, a recommendation is made to use the modified DECODE method with small sample sizes.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/28750

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