Multilevel functional clustering analysis.
In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approach. We develop and compare a hard clustering method applied to the scores derived from the MFPCA and a soft clustering method using an MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and the cluster patterns under a series of settings: small versus moderate number of time points; various noise levels; and varying number of subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes activated by immunity system cells. Prevalent response patterns are identified by clustering the expression profiles using our multilevel clustering analysis.
Georgia Institute of Technology, Atlanta, Georgia 30332, USA. nserban@isye.gatech.edu
Nicoleta
N
Serban
Huijing
H
Jiang
D000465:Algorithms; D001699:Biometry; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D007113:Immunity, Innate / Q000235:genetics; D008264:Macrophages / Q000276:immunology / Q000378:metabolism / Q000382:microbiology; D008954:Models, Biological; D015233:Models, Statistical; D025341:Principal Component Analysis