A novel data mining method to identify assay-specific signatures in functional genomic studies.

ABSTRACT:

Background

The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies.

Results

The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and expl

SUBMITTER: Rollins DK 

PROVIDER: S-EPMC1599756 | biostudies-literature | 2006 Aug

REPOSITORIES: biostudies-literature