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gimmR is the R package for analyzing genomics data using Gaussian Infinite Mixture Models (GIMM). Various versions of GIMMs are described in the papers below. The package is designed for and can be ran on a Linux-based system. It uses a pre-compiled executables for running the Gibbs samples (gimm) and processing of the Gibbs sampler outputs (posthoc).

The web application for online analysis based using GIMMs (WebGimm) can be accessed here http://eh3.uc.edu/apps/webgimm/

Freudenberg JM, Sivaganesan S, Wagner M, Medvedovic M. A semi-parametric Bayesian model for unsupervised differential co-expression analysis. BMC Bioinformatics 11:234. 2010. (Support Page)(Software)

Liu X, Jessen W, Sivaganesan S, Aronow BJ, Medvedovic M: Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. BMC Bioinformatics, 8(1):283. 2007.

Liu, X., Sivaganesan, S., Yeung, K.Y., Guo, J., Bumgarner, R.E., Medvedovic M. Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray datasets. (Bioinformatics 22:1737-44. 2006.) (Support Page)(preprint).

Medvedovic, M., Yeung, K.Y., Bumgarner, R.E. Bayesian Mixtures for Clustering Replicated Microarray Data. Bioinformatics. 20: 1222-1232, 2004.(PDF)

Medvedovic M., Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 18: 1194-1206, 2002. (PDF)

Medvedovic M. Identifying statistically significant patterns of expression via Bayesian Infinite Mixture Models. Critical Assessment of Microarray Data Analysis (CAMDA) 2000. (PDF)