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Open Access Highly Accessed Methodology article

Conditional clustering of temporal expression profiles

Ling Wang1, Monty Montano2, Matt Rarick2 and Paola Sebastiani3*

Author Affiliations

1 Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA

2 Section of Infectious Diseases, Center for HIV-1/AIDS Care and Research, Boston University School of Public Health, Boston, MA 02118, USA

3 Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA

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BMC Bioinformatics 2008, 9:147  doi:10.1186/1471-2105-9-147

Published: 11 March 2008



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


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 patterns 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 two or more experimental conditions, and genes that have a unique temporal profile in 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 genes with common and unique profiles. We also use the algorithm to characterize the response of human T cells to stimulations of antigen-receptor signaling gene expression temporal 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 uniquely stimulated genes from commonly stimulated genes in response to variable stimuli. Software for using this clustering method is available from the project home page.