Conditional clustering of temporal expression profiles.

ABSTRACT:

Background

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.

Results

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.

Conclusion

SUBMITTER: Wang L 

PROVIDER: S-EPMC2335301 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature