Significance analysis of time course microarray experiments
Significance analysis of time course microarray experiments
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DOI：
10.1073/pnas.0504609102
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发表时间：
2005-09-06
影响因子：
11.1
通讯作者：
Davis, RW
中科院分区：
文献类型：
--
作者：
Storey, JD;Xiao, WZ;Davis, RW
表征基因表达的全基因组动态调控是重要的，并且在未来会备受关注。然而，目前在时间进程研究中还没有确定的鉴定差异表达基因的方法。在此我们提出一种用于分析时间进程微阵列研究的显著性方法，该方法可应用于典型的比较类型和采样方案。这种方法应用于两项针对人类的研究。在一项研究中，鉴定出了在体内给予内毒素后随时间呈现差异表达的基因。通过使用我们的方法，在1%的错误发现率水平下有7409个基因被判定为显著，而几种现有的方法未能鉴定出任何基因。在另一项研究中，在10%的错误发现率水平下鉴定出了417个基因，这些基因在肾皮质中的表达随年龄变化。这里还表明，多达47%的基因随年龄变化的方式比简单的指数增长或衰减更为复杂。这里提出的方法已在免费分发且开源的EDGE软件包中实现。
Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source EDGE software package.