Bayesian mixture model based clustering of replicated microarray data

Bayesian mixture model based clustering of replicated microarray data

MOTIVATION Identifying patterns of co-expression in microarray data by cluster analysis has been a productive approach to uncovering molecular mechanisms underlying biological processes under investigation. Using experimental replicates can generally improve the precision of the cluster analysis by reducing the experimental variability of measurements. In such situations, Bayesian mixtures allo...

Clustering time-course Microarray data using functional Bayesian infinite mixture model

This paper presents a new Bayesian, infinite mixture model based, clustering approach specifically designed for time-course microarray data. The problem is to group together genes which have “similar” expression profiles given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a nonparametric regression ...

Bayesian infinite mixture model based clustering of gene expression profiles

MOTIVATION The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS We developed a clustering procedur...

Replicated Microarray Data

A mixture model-based approach to the clustering of microarray expression data

MOTIVATION This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much ...