Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data.

ABSTRACT:

Background

With the advance of microarray technology, several methods for gene classification and prognosis have been already designed. However, under various denominations, some of these methods have similar approaches. This study evaluates the influence of gene expression variance structure on the performance of methods that describe the relationship between gene expression levels and a given phenotype through projection of data onto discriminant axes.

Results

We compared Between-Group Analysis and Discriminant Analysis (with prior dimension reduction through Partial Least Squares or Principal Components Analysis). A geometric approach showed that these two methods are strongly related, but differ in the way they handle data structure. Yet, data structure helps understandi

SUBMITTER: Truntzer C 

PROVIDER: S-EPMC1831790 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature