Statistical Estimation of Circadian Time Using Gene Expression Data

Abstract

This dissertation develops statistical methods and evaluation standards for estimating circadian time using high-throughput gene expression data. The first paper introduces an alternating weighted least squares framework that robustly infers circadian time, with strong generalization across tissues and platforms. The second paper extends this framework by incorporating batch adjustment through low-rank latent factors, yielding accurate performance on both simulated and real batch-confounded data. The third paper introduces a unified benchmarking framework that standardizes preprocessing, choice of assessment, and performance metrics, and evaluates a comprehensive selection of circadian time estimation algorithms across diverse contexts. Together, these studies both establish a comprehensive benchmarking approach for circadian time inference and demonstrate a modeling strategy that is robust, transferable, and state-of-the-art across real and simulated settings.