Clustering microarray data.
Even a simple, small-scale, microarray experiment generates thousands to millions of data points. Clearly, spreadsheets or plotting programs do not suffice for analysis of such large volumes of data, and comprehensive analysis requires systematic methods for selection and organization of data. This chapter focuses on the concepts and algorithms of hierarchical clustering and the most commonly employed methods of partitioning or organizing microarray data, and freely available software that implements these algorithms.
Gollub
Jeremy
J
Department of Biochemistry, Stanford University Medical School, Stanford, CA, USA.
Sherlock
Gavin
G
D000818:Animals; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D012984:Software