title: Analysis and visualization of gene expression data using self-organizing maps.
abstract: Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U-matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.
authors.affiliation: Helsinki University of Technology, Neural Networks Research Centre, Finland. janne.nikkila@hut.fi
authors.forename: Janne
authors.initials: J
authors.lastname: Nikkilä
authors.forename: Petri
authors.initials: P
authors.lastname: Törönen
authors.forename: Samuel
authors.initials: S
authors.lastname: Kaski
authors.forename: Jarkko
authors.initials: J
authors.lastname: Venna
authors.forename: Eero
authors.initials: E
authors.lastname: Castrén
authors.forename: Garry
authors.initials: G
authors.lastname: Wong
mesh_terms: D016000:Cluster Analysis; D015870:Gene Expression / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015003:Yeasts / Q000235:genetics