Nikkilä Janne, Törönen Petri, Kaski Samuel, Venna Jarkko, Castrén Eero, Wong Garry
Helsinki University of Technology, Neural Networks Research Centre, Finland.
Neural Netw. 2002 Oct-Nov;15(8-9):953-66. doi: 10.1016/s0893-6080(02)00070-9.
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.
利用自组织映射（SOM）算法对从DNA微阵列获得的基因表达数据的聚类结构进行分析和可视化。SOM将数据非线性映射到二维映射网格，该网格可用作探索性数据分析工具，以生成关于基因之间关系以及最终基因功能的假设。数据内的相似关系和聚类结构可以可视化并进行解释。通过计算酵母基因的SOM来演示这些方法。通过分析已知功能类别的基因在SOM上的分布来研究它们之间的关系，用U矩阵法可视化聚类结构，并根据基因表达谱的特性对聚类进行表征。最后表明，与多维缩放和层次聚类这两种替代方法相比，SOM以更可靠的方式可视化基因的相似性。
