Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats.
Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats.
复制标题
通过计算机辅助转换饮酒大鼠大脑的微阵列数据获得的交互式分子网络。
DOI：
10.1055/s-0029-1216348
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发表时间：
2009
影响因子：
4.3
通讯作者：
Matthäus F
中科院分区：
文献类型：
--
作者：
Matthäus F
疾病中差异表达基因的列表随着所有技术水平的改进而变得越来越全面。尽管有99%或95%置信区间的统计截止值，但基因的数量可以增加到数百甚至数千个，这几乎不符合研究人员的理解。这份报告描述了通过数学算法处理这些数据的一些方法。从53个微阵列（两个大脑区域（杏仁核和尾壳核），三个大鼠品系饮酒或禁欲）获得的基因列表已被使用。它们是通过Affyesterday芯片分析得出的，包含了大约6000个通过我们质量过滤器的基因。他们已经受到了四个数学方法的处理：（a）基本统计，（B）主成分分析，（c）层次聚类，（d）引入贝叶斯网络。结果表明，通过使用p值或对数比，它们最好细分为大脑区域，其次是对大鼠品系的相当好的区分，以及对饮酒与禁欲的最差区分。然而，尽管与饮酒的关系是最弱的信号，但人们试图将与饮酒相关的基因整合到贝叶斯网络中，以了解更多关于它们之间的相互关系。该研究表明，这里使用的工具对于（a）数据集的质量控制，（B）构建交互式（分子）网络非常有用，但（c）在将大量数据整合到网络中时存在局限性。该研究还表明，平衡实验条件的数量与动物的数量往往是关键。
Lists of differentially expressed genes in a disease have become increasingly more comprehensive with improvements on all technical levels. Despite statistical cutoffs of 99% or 95% confidence intervals, the number of genes can rise to several hundreds or even thousands, which is barely amenable to a researcher's understanding. This report describes some ways of processing those data by mathematical algorithms. Gene lists obtained from 53 microarrays (two brain regions (amygdala and caudate putamen), three rat strains drinking alcohol or being abstinent) have been used. They resulted from analyses on Affymetrix chips and encompassed approximately 6 000 genes that passed our quality filters. They have been subjected to four mathematical ways of processing:(a) basic statistics,(b) principal component analysis,(c) hierarchical clustering, and (d) introduction into Bayesian networks. It turns out, by using the p-values or the log-ratios, that they best subdivide into brain areas, followed by a fairly good discrimination into the rat strains and the least good discrimination into alcohol-drinking vs. abstinent. Nevertheless, despite the fact that the relation to alcohol-drinking was the weakest signal, attempts have been made to integrate the genes related to alcohol-drinking into Bayesian networks to learn more about their inter-relationships. The study shows, that the tools employed here are extremely useful for (a) quality control of datasets,(b) for constructing interactive (molecular) networks, but (c) have limitations in integration of larger numbers into the networks. The study also shows that it is often pivotal to balance out the number of experimental conditions with the number of animals.