Comparing Statistical Methods for Analyzing DNA Methylation Data
Department
Mathematics and Statistics
Major
Applied Mathematics and Economics
Research Advisor
Olbricht, Gayla R.
Advisor's Department
Mathematics and Statistics
Funding Source
Opportunities for Undergraduate Research Experiences (OURE)
Abstract
DNA Methylation occurs when a methyl group attaches to a cytosine base in the DNA strand. This methylation of cytosine bases plays a significant role in gene expression. It has also been shown that differences in DNA methylation patterns exist between healthy and diseased individuals. Because methylation patterns vary from person to person, this creates statistical challenges when trying to quantify differences between groups of individuals. In this project, we compare statistical methods for analyzing DNA Methylation microarray data with the goal of detecting methylation differences between low-grade and high-grade HIV patients.
Biography
Arielle Bodine is a senior in applied mathematics and economics. Active on campus, she is the recruitment chair for Delta Omicron Lambda service sorority, vice-president of S&T’s chapter of Kappa Mu Epsilon math honor society and a Sue Shear fellow. She is also a student writer for the Missouri S&T marketing and communications department and an undergraduate researcher for the department of economics.
Research Category
Research Proposals
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hallway
Presentation Date
11 Apr 2016, 9:00 am - 11:45 am
Comparing Statistical Methods for Analyzing DNA Methylation Data
Upper Atrium/Hallway
DNA Methylation occurs when a methyl group attaches to a cytosine base in the DNA strand. This methylation of cytosine bases plays a significant role in gene expression. It has also been shown that differences in DNA methylation patterns exist between healthy and diseased individuals. Because methylation patterns vary from person to person, this creates statistical challenges when trying to quantify differences between groups of individuals. In this project, we compare statistical methods for analyzing DNA Methylation microarray data with the goal of detecting methylation differences between low-grade and high-grade HIV patients.
