Partial mixture model for tight clustering of gene expression time-course.

ABSTRACT:

Background

Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.

Results

In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC2492882 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature