Dissertation Defense: Thien Pham

"Statistical Methods for the Design and Analysis of Circadian Omics Studies", School of Public Health Department of Biostatistics and Health Data Science. Advisor and Committee Chair: George Tseng Abstract: Circadian rhythms coordinate daily physiology and influence health and disease. Advances in high-throughput technologies have expanded circadian omics studies across tissues, species, and molecular modalities. Limited sample sizes and uneven sampling times require careful power analysis and study design. Comparisons between conditions help distinguish conserved from altered rhythmic programs and assess how well animal models recapitulate human circadian biology. Integrating evidence across tissues further reveals the temporal organization of these programs across organs and their conservation across species. This dissertation develops three statistical frameworks to address these connected needs. In Chapter 2, we develop Simulation-based Circadian Power (SCP), a framework for power analysis and sample size planning in circadian omics studies. SCP uses pilot-derived distributions of amplitude, phase, sampling time, and noise to capture transcriptome-wide signal heterogeneity. It estimates power under false discovery rate control for rhythmicity detection and two-group differential circadian analyses, supporting both cosinor and non- cosinor methods. Bootstrap analysis quantifies uncertainty arising from pilot data. SCP also provides 160 curated circadian transcriptomic datasets spanning more than 100 tissues across multiple species to support pilot calibration and study design. Determining whether rhythmic programs are conserved requires more than testing for differences between conditions. In Chapter 3, we introduce BayesCR (Bayesian Comparative Rhythmicity), a Bayesian framework for evaluating cross-condition congruence at the gene, pathway, and genome-wide levels. Posterior rhythmicity probabilities and Bayesian differential and equivalence tests identif