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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Significance analysis of time course microarray experiments
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Significance analysis of time course microarray experiments

机译：时程微阵列实验的意义分析

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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.
机译：表征基因表达范围的全基因组动态调节非常重要，并且将来会引起广泛关注。但是，在时程研究中，目前尚没有确定差异表达基因的确定方法。在这里，我们提出了一种用于分析时程微阵列研究的重要方法，该方法可应用于比较和采样方案的典型类型。该方法应用于两项关于人体的研究。在一项研究中，鉴定出响应体内内毒素施用而随时间显示差异表达的基因。通过使用我们的方法，以1％的错误发现率水平将7409个基因称为显着基因，而现有的几种方法都无法识别任何基因。在另一项研究中，以10％的错误发现率水平鉴定出417个基因，这些基因显示表达随年龄在肾皮质中变化。在此还表明，多达47％的基因随着年龄的增长以比简单的指数式增长或衰退更为复杂的方式变化。此处提出的方法已在免费分发的开源EDGE软件包中实现。

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