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

Array technologies have made it straightforward to monitor simultaneously t he expression pattern of thousands of genes. The challenge now is to interp ret such massive data sets. The first step is to extract the fundamental pa tterns of gene expression inherent in the data. This paper describes the ap plication of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features i n complex, multidimensional data. The method has been implemented in a publ icly 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, and NB4 cells). Expressio n patterns of some 6,000 human genes were assayed, and an online database w as created. GENECLUSTER was used to organize the genes into biologically re levant clusters that suggest novel hypotheses about hematopoietic different iation-for example, highlighting certain genes and pathways involved in "di fferentiation therapy" used in the treatment of acute promyelocytic leukemi a.