Mixture modelling of gene expression data from microarray experiments

Abstract

Motivation: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. Results: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al. , Nature , 406, 536–540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al. , 2001, submitted). Availability: Macros written in the R language implementing the methods in this report can be obtained at the first author’s website: http://www.sph.umich.edu/~ghoshd/COMPBIO/mixture1/index.html. Contact: ghoshd@umich.edu