Conference Program

CircadianPipeline: A pipeline for differential rhythmicity analysis in R/Shiny (310223)

George C. Tseng, Department of Biostatistics, School of Public Health, University of Pittsburgh 
*Xiangning Xue, University of Pittsburgh  
Wei Zong, Department of Biostatistics, University of Pittsburgh 

Keywords: Circadian analysis

The oscillation in the expression of circadian genes regulates the daily physiological processes. The disruption of circadian patterns caused by disease status or other behaviors drives the study of the differential rhythmicity in two contrasting groups. We propose CircadianPipeline, a solid statistical pipeline based on the cosinor model. The pipeline identifies rhythmicity characteristics including (1) stark change in rhythm status, i.e., a gene being rhythmic in one group but not in the other, and (2) change of rhythmicity parameters including amplitude, phase, rhythm-adjusted mean (MESOR), and signal to noise ratio. Our pipeline provides biological meaningful step-wise analysis with simulation-proved control of type I error at each step. We implemented the method in both R package and R shiny with a user-friendly protocol and interface. We also provide extensive visualization tools to present the differential signals.