Welcome! This is the supporting webpage for the article:
Bayesian Conditional Clustering of Temporal Expression Profiles.
L. Wang, M. Montano, M. Rarick and P. Sebastiani.
This article presents a novel technique to cluster data from time course microarray experiments performed across several experimental conditions. Our algorithm uses polynomial models to describe the gene expression pattern over time, a full Bayesian approach with proper conjugate priors to make the algorithm invariant to linear transformations, and an iterative procedure to identify genes that have a common temporal expression profile across experimental conditions, and genes that have a unique temporal profile under a specific condition. We use simulated data to evaluate the effectiveness of this new algorithm in finding the correct number of clusters and in identifying the genes that have common profiles in at least two experimental conditions and gene that have unique profiles in a specific experimental condition. We use this algorithm to cluster gene expression temporal profiles measured in sixdifferent biological conditions to identify common and unique genes.
Please use the navagation bars on the top to browse the conditional clustering program, the simulated data and results.
For questions and concerns, please send an email to Ling Wang: wangling@bu.edu. Thank you.