title: Basic microarray analysis: grouping and feature reduction.
abstract: DNA microarray technologies are useful for addressing a broad range of biological problems - including the measurement of mRNA expression levels in target cells. These studies typically produce large data sets that contain measurements on thousands of genes under hundreds of conditions. There is a critical need to summarize this data and to pick out the important details. The most common activities, therefore, are to group together microarray data and to reduce the number of features. Both of these activities can be done using only the raw microarray data (unsupervised methods) or using external information that provides labels for the microarray data (supervised methods). We briefly review supervised and unsupervised methods for grouping and reducing data in the context of a publicly available suite of tools called CLEAVER, and illustrate their application on a representative data set collected to study lymphoma.
authors: Stanford Medical Informatics, Department of Medicine, Stanford University, 251 Campus Drive, MSOB X-215, Stanford, CA 94305-5479, USA.
authors: S
authors: S
authors: Raychaudhuri
authors: P D
authors: PD
authors: Sutphin
authors: J T
authors: JT
authors: Chang
authors: R B
authors: RB
authors: Altman
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D020407:Internet; D008223:Lymphoma / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software
chemical_list: D012333:RNA, Messenger