Mixtures of Regression Models for Time-Course Gene Expression Data: Evaluation of Initialization and Random Effects

Finite mixture models are routinely applied to time course
microarray data. Due to the complexity and size of this type of
data the choice of good starting values plays an important role. So
far initialization strategies have only been investigated for data
from a mixture of multivariate normal distributions. In this work
several initialization procedures are evaluated for mixtures of
regression models with and without random effects in an extensive
simulation study on different artificial datasets. Finally these
procedures are also applied to a real dataset from E. coli.