Perelman School of Medicine Events Calendar

Richard J. Schwab, MD
"Overview of Sleep Disorders: Part I"

"Sparse Semiparametric Nonlinear Models with Applications to Chromatographic Fingerprinting"

Michael Wierzbicki
PhD Candidate
Division of Biostatistics
University of Pennsylvania Perelman School of Medicine

Abstract: Medicinal herbs are comprised of a multitude of compounds and the identification of their active composition is an important area of research. High Performance Liquid Chromatography, a popular technique to detect compounds in herbs, outputs a chromatogram, a curve characterized by spikes corresponding to detected compounds. As the particular set of compounds is unique to each herb and spike locations can be used to identify compounds, chromatograms provide a visual representation, or fingerprint, of herbs. Constructing statistical models for the estimation and comparison of chromatographic fingerprints is difficult due to the sparse, spiky nature of chromatograms. Moreover, across different experimental conditions, the location of spikes can be shifted, preventing the establishment of a standardized fingerprint, direct comparison of curves, and efficient compound identification. Here we describe a sparse semiparametric nonlinear mixed effects modeling framework for the registration and estimation of functional data with sparse structures. Data-driven basis expansion is used to model group-averaged curves while parametric modeling of time warping functions aligns curves. Penalized estimation with the Adaptive Lasso penalty provides a unified criterion for curve registration, model selection, and estimation. Furthermore, the Adaptive Lasso estimators possess attractive sampling properties. The performance of the modeling framework and its role in medicinal herb research are demonstrated through its application to two chromatographic data sets.


Dissertation Advisor: Wensheng Guo, PhD
Committee Chair: Warren Bilker, PhD
Committee: Sarah Ratcliffe, PhD, Bruce Turetsky, MD