Statistical Methods for Circadian Analysis on Omics DataXue, Xiangning (2024) Statistical Methods for Circadian Analysis on Omics Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractCircadian rhythms are endogenous processes that synchronize biological activities to a 24-hour day-light cycle. Circadian rhythms exhibit significant variations across individuals, influenced by factors such as sex and age, and are also found to be linked to health status, particularly in psychological diseases. Circadian analysis is performed to identify differential rhythmicity like oscillating amplitude and phase between healthy controls and subjects with diseases to explore the underlying molecular mechanism and etiology of dysfunctions. In this thesis, the first paper presents a comprehensive and interactive pipeline to capture the multifaceted characteristics of differentially rhythmic biomarkers with accurately controlled type I errors. Analysis outputs are accompanied by informative visualization and interactive exploration. The workflow is demonstrated in a real-world study and is extensible to general omics circadian applications. In human studies, a subject's internal circadian clock may differ from recorded time due to measurement error or subject-specific variability. To accommodate this variability, the second paper develops a unified Bayesian model to simultaneously re-estimate each subject's molecular circadian time and detect circadian genes. In the third paper, we extend the Bayesian circadian model to integrate multiple cohorts (e.g., multiple brain regions). Biomarker detection by Bayes factor allows integration of homogeneous information across cohorts while distinguishes heterogeneous circadian signals in individual cohort. Share
Details
MetricsMonthly Views for the past 3 yearsPlum AnalyticsActions (login required)
|
![[img]](http://d-scholarship.pitt.edu/style/images/fileicons/application_pdf.png)
![[feed]](/images/feed-icon-32x32.png){kind=icon}