A Dirichlet process mixture model for clustering longitudinal gene expression data.

ABSTRACT: 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. 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 clustering 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 con

SUBMITTER: Sun J 

PROVIDER: S-EPMC5583037 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature