Basic microarray analysis: grouping and feature reduction

DNA microarray technologies are useful for addressing a broad range of biol ogical problems - including the measurement of mRNA expression levels in ta rget cells. These studies typically produce large data sets that contain me asurements on thousands of genes under hundreds of conditions. There is a c ritical need to summarize this data and to pick out the important details. The most common activities, therefore, are to group together microarray dat a and to reduce the number of features. Both of these activities can be don e using only the raw microarray data (unsupervised methods) or using extern al information that provides labels for the microarray data (supervised met hods). We briefly review supervised and unsupervised methods for grouping a nd reducing data in the context of a publicly available suite of tools call ed CLEAVER, and illustrate their application on a representative data set c ollected to study lymphoma.