Masters Theses

Abstract

"The prevalence of gene expression microarray datasets in public repositories gives opportunity to analyze biologically interesting datasets without running the laboratory aspect in house. Such experimentation is expensive in terms of finances, time, and expertise, which often results in low numbers of replicates. Meta-analysis techniques attempt to overcome issues due to few biological or technical replicates by combining separate experiments together to increase statistical power. Proper statistical considerations help to offset issues like simultaneous testing of thousands of genes, unintended hybridization, and other noises.

Microarrays contain light intensities from tens of thousands of hybridized probes giving a measure of gene expression for much of the human genome. This work focuses on identifying differentially expressed genes between obese and non-obese patients using microarray data from two studies collected from mesenchymal stem cell samples. Obesity is associated with poorer quality stem cells that are less readily available to differentiate and it is of interest to identify genes associated with this condition. Meta-analysis performed to increase statistical power from low replicate microarray experiments is an attempt to gain a better idea of the gene expression differences between obese and non-obese individuals compared to results from an individual study. Increased statistical power translates to improved ability to discover genes or sets of genes associated with this observed decrease in differentiation efficacy. Furthermore, pathway analysis could be completed to identify pathways of interest from this differential expression analysis"--Abstract, p. iii

Advisor(s)

Olbricht, Gayla R.

Committee Member(s)

Adekpedjou, Akim
Semon, Julie A.

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2023

Pagination

x, 67 pages

Note about bibliography

Includes_bibliographical_references_(pages 62-66)

Rights

© 2023 Dakota William Shields, All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12301

Electronic OCLC #

1426307731

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