Gene microarray analysis using angular distribution decomposition.
Gene microarray analysis using angular distribution decomposition.
复制标题
使用角分布分解进行基因微阵列分析。
DOI：
10.1089/cmb.2006.0098
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Lees K
中科院分区：
文献类型：
--
作者：
Lees K
聚类技术已广泛应用于微阵列数据分析，对具有相似表达谱的基因进行分组。表达谱的相似性以及聚类的结果在很大程度上取决于数据是如何转换的。我们提出了一种利用条件对之间的相对表达变化和角度变换来定义基因表达模式相似性的方法。可以选择实验条件的两两比较来反映聚类的目的，从而控制基因之间相似性的定义。然后使用变分贝叶斯混合建模方法在转换后的数据中找到聚类。微阵列数据分析的目的通常是定位显示特定表达变化模式的基因组，并在这些组中定位可能需要进一步实验研究的特定靶基因。我们展示了角变换将数据映射到一种表示，从这种表示中可以自动挖掘相对规则变化的信息。这些信息可以用来理解表达变化的“特征”，这对不同的集群很重要，从而可以很容易地定位潜在的有趣的集群。最后，我们展示了如何通过表达模式和强度变化来可视化集群内的基因，从而在感兴趣的集群中突出显示潜在的靶基因。
Clustering techniques have been widely used in the analysis of microarray data to group genes with similar expression profiles. The similarity of expression profiles and hence the results of clustering greatly depend on how the data has been transformed. We present a method that uses the relative expression changes between pairs of conditions and an angular transformation to define the similarity of gene expression patterns. The pairwise comparisons of experimental conditions can be chosen to reflect the purpose of clustering allowing control the definition of similarity between genes. A variational Bayes mixture modeling approach is then used to find clusters within the transformed data. The purpose of microarray data analysis is often to locate groups genes showing particular patterns of expression change and within these groups to locate specific target genes that may warrant further experimental investigation. We show that the angular transformation maps data to a representation from which information, in terms of relative regulation changes, can be automatically mined. This information can be then be used to understand the "features" of expression change important to different clusters allowing potentially interesting clusters to be easily located. Finally, we show how the genes within a cluster can be visualized in terms of their expression pattern and intensity change, allowing potential target genes to be highlighted within the clusters of interest.