Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments.

ABSTRACT:

Background

Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together.

Results

We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short time intervals following deafferentation of olfactory receptor neurons. N

SUBMITTER: Liu H 

PROVIDER: S-EPMC1127068 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature