Microarray data mining using landmark gene-guided clustering.

ABSTRACT:

Background

Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset.

Results

By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters

SUBMITTER: Chopra P 

PROVIDER: S-EPMC2262871 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature