Clustering of temporal gene expression data with mixtures of mixed effects models with a penalized likelihood.

ABSTRACT:

Motivation

Clustering algorithms like K-Means and standard Gaussian mixture models (GMM) fail to account for the structure of variability of replicated data or repeated measures over time. Additionally, a priori cluster number assumptions add an additional complexity to the process. Current methods to optimize cluster labels and number can be inaccurate or computationally intensive for temporal gene expression data with this additional variability.

Results

An extension to a model-based clustering algorithm is proposed using mixtures of mixed effects polynomial regression models and the EM algorithm with an entropy penalized log-likelihood function (EPEM). The EPEM is used to cluster temporal gene expression data with this additional variability. The addition of random effect

SUBMITTER: Lu D 

PROVIDER: S-EPMC6394398 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature