This is the supporting webpage for the article:
Conditional Clustering of Temporal Expression Profiles.
Wang, M. Montano, M. Rarick and P. Sebastiani.
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.
use the navagation bars on the top to browse the conditional clustering
program, the simulated data and results.
questions and concerns, please send an email to Ling Wang: email@example.com.