Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns.
Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.
Herrero
Javier
J
Bioinformatics Unit, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain.
Dopazo
Joaquín
J
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D005800:Genes, Fungal / Q000235:genetics; D016678:Genome; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors