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
Gene expression technologies allow expression levels to be compared across treatments for thousands of genes simultaneously. Asymmetry in the empirical distribution of the test statistics from the analysis of a gene expression experiment is often observed. Statistical methods exist for identifying differentially expressed (DE) genes while controlling multiple testing error while taking into account the asymmetry of the distribution of the effect sizes. This paper compares three statistical methods (Modified Q-value, Modified SAM, and Asymmetric Local False Discovery Rate) used to identify differentially expressed (DE) genes that take into account such patterns while controlling false discovery rate (FDR). The results of the simulation studies performed suggest that the Modified Q-values outperforms the other methods most of the time and also better controls the FDR.
Recommended Citation
Tchakounte Wakem, Seguy, "A Comparison of Methods Taking into Account Asymmetry when Evaluating Differential Expression in Gene Expression Experiments" (2018). Statistics. 51.
https://digitalcommons.ndsu.edu/stats/51
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/28874