DOI
https://doi.org/10.25772/Y0R8-YW30
Defense Date
2015
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Biostatistics
First Advisor
Kellie J. Archer
Second Advisor
Nitai D. Mukhopadhyay
Third Advisor
Nak-Kyeong Kim
Fourth Advisor
James P. Bennett Jr.
Fifth Advisor
Jintanat Ananworanich
Abstract
Combining effect sizes from individual studies using random-effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. We proposed two new methods for random effects estimation and measurements for model variation and strength of the study heterogeneity. We then developed a statistical technique to test for significance of random effects and identify heterogeneous genes. We also proposed another meta-analytic approach that incorporates informative weights in the random effects meta-analysis models. We compared the proposed methods with the standard and existing meta-analytic techniques in the classical and Bayesian frameworks. We demonstrate our results through a series of simulations and application in gene expression neurodegenerative diseases.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
12-5-2015
Included in
Applied Statistics Commons, Bioinformatics Commons, Biometry Commons, Biostatistics Commons, Microarrays Commons, Neurosciences Commons, Statistical Models Commons, Translational Medical Research Commons