Fold change and p-value cutoffs significantly alter microarray interpretations.
Fold change and p-value cutoffs significantly alter microarray interpretations.
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DOI：
10.1186/1471-2105-13-s2-s11
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发表时间：
2012-03-13
影响因子：
3
通讯作者：
Duan ZH
中科院分区：
文献类型：
--
作者：
Dalman MR;Deeter A;Nimishakavi G;Duan ZH
正如环境对基因表达很重要一样，微阵列预处理对转录组学也很重要。微阵列数据存在规范化和显著性问题。>2的任意折叠变化（FC）截止值和显著性p值<0.02导致数据收集只关注在其他基因之间差异很大的基因。因此，问题就出现了，在解释中，是生物学的界限更重要，还是统计的界限更重要。在本文中，我们使用genesspring和不同的差异基因表达切断重新分析了斑马鱼（D. rerio）微阵列数据集，发现数据解释有很大不同。此外，尽管微阵列技术取得了进步，但该阵列捕获了大部分已知的基因，但在检测的基因数量中仍然留下了很大的空白，例如瘦素，一种与缺氧诱导的血管生成直接相关的多向激素。这些数据强烈表明，差异表达基因的数量上调多于下调，许多基因表明对先前已知功能的保守信号。Marques等人（2008）总结的数据相似，但令人惊讶的是，一些基因显示出意想不到的信号，这些信号可能是组织（心脏）的产物，也可能是预期的反应是短暂的。我们的分析表明，基于选择的统计或折叠变化截止值；微阵列分析基本上可以提供不止一个答案，这意味着数据解释更像是一门艺术，而不是一门科学，后续的基因表达研究是必须的。此外，基因芯片的注释和开发不仅需要与新基因组的测序保持同步，还需要与对整个基因芯片解释至关重要的新基因保持同步。
As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis. The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient. Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.