Finding gene clusters for a replicated time course study.

ABSTRACT:

Background

Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies.

Findings

In this paper, we

SUBMITTER: Qin LX 

PROVIDER: S-EPMC3906880 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature