References

1. Bioinformatics. 2007 May 7; [Epub ahead of print]
CRCView: A web server for analyzing and visualizing microarray gene expression data using model-based clustering.
Xiang Z, Qin Z, He Y.

Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI.

SUMMARY: CRCView is a user-friendly point-and-click web server for analyzing and visualizing microarray gene expression data using a Dirichlet process mixture model-based clustering algorithm. CRCView is designed to clustering genes based on their expression profiles. It allows flexible input data format, rich graphical illustration, as well as integrated GO term based annotation/interpretation of clustering results. AVAILABILITY: crcview.hegroup.org/

PMID: 17485426

2. Bioinformatics. 2006 Aug 15;22(16):1988-97. Epub 2006 Jun 9.
Clustering microarray gene expression data using weighted Chinese restaurant process.
Qin ZS.

Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu

MOTIVATION: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging. RESULTS: We devised an improved model-based Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance. AVAILABILITY: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/.

PMID: 16766561