Clustering of the SOM easily reveals distinct gene expression patterns

[编者的话]

BMC上新鲜出炉的文章，其中SOM方法的应用值得学习。

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 e.g. 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. Hierarchical clustering and K-means clustering is then further 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 patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described on the original publication by supervised clustering analysis, whereas the fourth one was novel.

Conclusions

Our study shows that using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The summarized “expression display” allows the clinician to evaluate the classification options rather than giving a fixed decision, was reflecting the real-world situation.

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