ABSTRACT
Transcript profiling by microarray analysis offers a great opportunity to reveal unknown effects in a comprehensive context. To be able to interpret the data, some basic issues in experimental setting and design including type and number of replications have to be considered and are discussed in this work. In order to facilitate and automate data interpretation, the experimental data were projected and clustered by Correspondence Analysis, subsequently associated with gene ontology (GO) terms for functional classification. We applied the technology to investigate copper metabolism in the human pathogen Candida albicans. The presented dataset gives an example of how different fluorescent labeling, biological and technical replicas and data analysis strategies for microarray experiments may influence the final outcome of the results.
