  Subgroup identification (clustering) is an important problem in biomedical
research. Gene expression profiles are commonly utilized to define subgroups.
Longitudinal gene expression profiles might provide additional information on
disease progression than what is captured by baseline profiles alone. Moreover,
the longitudinal gene expression data allows for intra-individual variability
to be accounted for when grouping patients. Therefore, subgroup identification
could be more accurate and effective with the aid of longitudinal gene
expression data. However, existing statistical methods are unable to fully
utilize these data for patient clustering. In this article, we introduce a
novel subgroup identification method in the Bayesian setting based on
longitudinal gene expression profiles. This method, called BClustLonG, adopts a
linear mixed-effects framework to model the trajectory of genes over time while
clustering is jointly conducted based on the regression coefficients obtained
from all genes. In order to account for the correlations among genes and
alleviate the high dimensionality challenges, we adopt a factor analysis model
for the regression coefficients. The Dirichlet process prior distribution is
utilized for the means of the regression coefficients to induce clustering.
Through extensive simulation studies, we show that BClustLonG has improved
performance over other clustering methods. When applied to a dataset of
severely injured (burn or trauma) patients, our model is able to distinguish
burn patients from trauma patients and identify interesting subgroups in trauma
patients.
