Microarray data analysis

http://discover.nci.nih.gov/microarrayAnalysis/Exploratory.Analysis.jsp

Exploratory Analysis
Pattern-Finding
Exploratory analysis aims to find patterns in the data that aren’t predicted by the experimenter’s current knowledge or pre-conceptions. Some typical goals are to identify groups of genes expression patterns across samples are closely related; or to find unknown subgroups among samples. A useful first step in all analyses is to identify outliers among samples – those that appear suspiciously far from others in their group. To address these questions, researchers have turned to methods such as cluster analysis, and principal components analysis, although these have often been used inappropriately.
The first widely publicized microarray studies aimed to find uncharacterised genes, which act at specific points during the cell cycle. Clustering is the natural first step in doing this. Unfortunately many people got the impression that clustering is the 'right' thing to do with microarray data; the confusion has been perpetuated, since many software packages have catered to this impression. The proper way to analyze data is the way that addresses the goal at which the study was aimed. Clustering is a useful exploratory technique for suggesting resemblances among groups of genes, but it’s not a way of identifying the differentially regulated genes in an experimental study.