Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study

Background: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples. Results: We tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression pattern...

• Type: Journal
• Year: 2002
• Where: BMCBI
• Authors: Junbai Wang, Jan Delabie, Hans Christian Aasheim, Erlend Smeland, Ola Myklebost
• Keywords: BMCBI 2002, Expression Data, Gene Expression, Gene Expression Patterns