Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.

ABSTRACT: Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat,

SUBMITTER: Tamayo P 

PROVIDER: S-EPMC15868 | biostudies-literature | 1999 Mar

REPOSITORIES: biostudies-literature