Department of Biology, University of Akron, Akron, OH, USA.
BMC Bioinformatics. 2012 Mar 13;13 Suppl 2(Suppl 2):S11. doi: 10.1186/1471-2105-13-S2-S11.
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.
由于上下文对基因表达很重要，因此微阵列向转录组学的预处理也很重要。微阵列数据存在多种归一化和显著性问题。任意的倍数变化（FC）>2 的截止值和显著性 p 值<0.02 导致数据收集只关注在其他基因中变化剧烈的基因。因此，人们开始质疑在解释过程中，生物学还是统计截止值更为重要。在本文中，我们使用 GeneSpring 重新分析了一个斑马鱼（D. rerio）微阵列数据集，并使用不同的差异基因表达截止值，发现数据解释有很大的不同。此外，尽管微阵列技术取得了进步，但该阵列捕获了大量已知的基因，但仍有大量基因在检测数量上存在空白，例如瘦素 a 一种与缺氧诱导的血管生成直接相关的多效激素。
数据强烈表明，差异表达基因的数量上调多于下调，许多基因表明保守信号与先前已知的功能有关。与 Marques 等人（2008 年）的重现数据相似，但令人惊讶的是，一些基因显示出意想不到的信号，这可能是组织（心脏）的产物，或者预期的反应是短暂的。
我们的分析表明，基于所选的统计或倍数变化截止值；微阵列分析可以提供本质上超过一个答案，暗示数据解释更像是一门艺术而不是科学，必须进行后续的基因表达研究。此外，基因芯片注释和开发不仅需要跟上新基因组测序的步伐，还需要跟上对整体基因芯片解释至关重要的新基因的步伐。
