Conditional clustering of temporal expression profiles

Many microarray experiments produce temporal profiles in different biological conditions butcommon cluster techniques are not able to analyze the data conditional on the biological conditions.

Results: This article presents a novel technique to cluster data from time course microarray experimentsperformed across several experimental conditions. Our algorithm uses polynomial models to describe the geneexpression patterns over time, a full Bayesian approach with proper conjugate priors to make the algorithminvariant to linear transformations, and an iterative procedure to identify genes that have a common temporalexpression profile across two or more experimental conditions, and genes that have a unique temporal profile in a specific condition.

Conclusions: We use simulated data to evaluate the effectiveness of this new algorithm in finding the correctnumber of clusters and in identifying genes with common and unique profiles.

We also use the algorithm tocharacterize the response of human T cells to stimulations of antigen-receptor signaling gene expressiontemporal profiles measured in six different biological conditions and we identify common and unique genes.These studies suggest that the methodology proposed here is useful in identifying and distinguishing uniquelystimulated genes from commonly stimulated genes in response to variable stimuli. Software for using thisclustering method is available from http://people.bu.edu/sebas/condclust/ccindex.htm.

Author: Ling Wang, Monty Montano, Matt Rarick and Paola Sebastiani
Credits/Source: BMC Bioinformatics 2008, 9:147

Published on: 2008-03-11

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