Position-based clustering of microarray expression data.
Position-based clustering of microarray expression data.
复制标题
微阵列表达数据的基于位置的聚类。
DOI：
10.1101/pdb.prot5280
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发表时间：
2009
影响因子：
--
通讯作者：
Kim,Joomyeong
中科院分区：
文献类型：
--
作者：
Faulk,Christopher;Kim,Joomyeong
微阵列彻底改变了大规模捕获基因表达数据的方式，使成千上万的转录本表达水平得以快速、廉价地定量。随着微阵列扩展到单核苷酸多态性（SNP）、基因拷贝数变异和甲基化的研究，对结果的分析和理解变得更加重要（Trevino et al. 2007）。分析这些雪崩数据的最直接方法是寻找实验条件之间变化最大的基因。这种主要的倍数变化通常是研究人员看到的第一个数据，并用于建立受影响最严重的基因的“顶级基因列表”。二级分析通常通过基因本体（GO）分类、生物学途径、聚类分析或其他方法对基因进行分组来进行（Beissbarth 2006; Quackenbush 2006）。二次分析的目的是从生成的大量表格中提取更多具有生物学意义的信息。
DISCUSSIONMicroarrays have revolutionized the large-scale capture of gene expression data, allowing tens of thousands of transcript expression levels to be quantified quickly and cheaply. With the expansion of microarrays to the study of single nucleotide polymorphisms (SNPs), gene copy number variation, and methylation, analysis and understanding of the results is even more critical (Trevino et al. 2007). The most direct way to analyze this avalanche of data is to look for the genes with the highest change between experimental conditions. This primary fold change is often the first data a researcher sees and is used to build “top gene lists” of the most affected genes. A secondary analysis is usually performed by grouping genes by Gene Ontology (GO) classification, by biological pathway, by cluster analysis, or by other methods (Beissbarth 2006; Quackenbush 2006). The purpose of the secondary analysis is to extract more biologically meaningful information from the vast tables produced.