Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.

ABSTRACT:

Background

A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we qua

SUBMITTER: Datta S 

PROVIDER: S-EPMC1590054 | biostudies-literature | 2006 Aug

REPOSITORIES: biostudies-literature