Statistics Seminar: STA 290
Thursday, April 19th, 2012 at 4.10pm, MSB 1147 (Colloquium Room)
Refreshments prior to seminar in MSB 4110 (Statistics Lounge)
Speaker: Nicoleta Serban (Georgia Institute of Technnology, Atlanta)
Title: Multilevel functional clustering analysis
Abstract: In this research project, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g. subjects) at multiple sub-units (e.g. proteins); that is, there are multiple random functions observed for each unit. To describe the within- and between-variability induced by the hierarchical structure in the data, we take a multilevel functional principal components (MFPCA) approach. We develop and compare a hard clustering method based on the scores derived from the multilevel FPCA and a soft clustering method using a MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and cluster patterns under a series of settings: small vs. moderate number of time points, various noise levels and varying number of repeated measurements or subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes of immune cells. Common and unique response patterns are identified by clustering the expression profiles using our multilevel clustering analysis.
This is joint work with Dr. Huijing Jiang at IBM Watson Research Center.