UCPH Statistics Seminar: Rosemary Braun

Speaker: Rosemary Braun from Northwestern University

Title: About Time: Quantifying dynamical changes in sparse, noisy, high-dimensional data

Abstract: The circadian clock orchestrates a vast array of behavioral and physiological processes with a 24-hour cycle, enabling organisms to anticipate and adapt to the Earth's day.  Entrainable by environmental cues, the rhythm itself is generated by a self-sustained molecular oscillator present in nearly every cell that governs the expression of thousands of genes, precisely coordinating biological processes at the microscopic scale in a manner that is both reliable yet flexible enough to adapt to environmental changes.  Today, high-throughput omics assays enable us to probe these mechanisms in molecular detail, with the goal of making inferences about which genes are under circadian control and how their dynamics change under different conditions.  Yet analyzing this transcriptomic time-series data raises new challenges: that of characterizing dynamics when the data are noisy, sparsely sampled in time, and may not be strictly periodic.  Current methods make unrealistic assumptions (such as stationarity of residuals) or rely on "template" waveforms that limit the scope of discovery.  In this talk, I will discuss our recent work on nonparametric methods to analyze circadian transcriptomic data and overcome these challenges by exploiting and extending results from dynamical systems theory and topological data analysis.