Analysis and visualization of gene expression data using Self-Organizing Maps

doi:10.1016/S0893-6080(02)00070-9

UM > Faculty of Health Sciences

Status	已發表Published
	Analysis and visualization of gene expression data using Self-Organizing Maps
	Nikkila J.1; Toronen P.2; Kaski S.1; Venna J.1; Castren E.2; Wong G.2
	2002-10-01
Source Publication	Neural Networks
Volume	15
Issue	8-9
Pages	953-966
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. © 2002 Elsevier Science Ltd. All rights reserved.
Keyword	Clustering Exploratory data analysis Gene expression Information visualization Self-Organizing Map
DOI	10.1016/S0893-6080(02)00070-9
URL	View the original
Language	英語English
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science View Full-Text via Scopus
Citation statistics
Document Type	Conference paper
Collection	Faculty of Health Sciences
Affiliation	1.Aalto University 2.Itä-Suomen yliopisto
Recommended Citation GB/T 7714	Nikkila J.,Toronen P.,Kaski S.,et al. Analysis and visualization of gene expression data using Self-Organizing Maps[C], 2002, 953-966.
APA	Nikkila J.., Toronen P.., Kaski S.., Venna J.., Castren E.., & Wong G. (2002). Analysis and visualization of gene expression data using Self-Organizing Maps. Neural Networks, 15(8-9), 953-966.

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