Masters Theses


"Microarray technology is a useful tool for studying the expression levels of thousands of genes or exons within a single experiment. Data from microarray experiments present many challenges for researchers since costly resources often limit the experimenter to small sample sizes and large amounts of data are generated. The researcher must carefully consider the appropriate statistical analysis to use that aligns with the experimental design employed. In this work, statistical issues are investigated and addressed for a microarray experiment that examines how expression levels change over time as individuals are sleep deprived. Over the course of 48 hours of sleep deprivation, RNA is collected from saliva samples of two study participants. These samples were hybridized to exon microarrays to measure gene and exon expression at three different time points. Five different statistical analyses are conducted to test for expression differences over time. These analyses are carefully scrutinized and a thorough investigation is conducted on the microarray data. The different analyses elicited different findings. Several genes and exons are identified as differentially regulated over time and should be examined closer with regard to their relationship to sleep deprivation"--Abstract, page iii.


Olbricht, Gayla R.

Committee Member(s)

Thimgan, Matthew S.
Paige, Robert


Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics


Missouri University of Science and Technology

Publication Date



xi, 99 pages

Note about bibliography

Includes bibliographical references (pages 97-98).


© 2013 Stephanie Marie Berhorst, All rights reserved.

Document Type

Thesis - Open Access

File Type




Subject Headings

Sleep deprivation -- Analysis
DNA microarrays -- Statistical methods
Gene expression -- Statistical methods

Thesis Number

T 10844

Electronic OCLC #