跨平台微阵列数据标准化用于调控网络推断。
Cross-platform microarray data normalisation for regulatory network inference.
机构信息
Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin, Ireland.
出版信息
PLoS One. 2010 Nov 12;5(11):e13822. doi: 10.1371/journal.pone.0013822.
BACKGROUND
Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.
METHODS
We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.
CONCLUSIONS
Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.
背景
从时间序列微阵列数据推断基因调控网络 (GRN) 受到可用时间序列的长度相对于网络中的大量基因较短而产生的维数问题的影响。为了克服这一问题，必须从不同的来源进行数据集成。来自不同来源和平台的微阵列数据是公开可用的，但由于平台和实验差异，集成并不简单。
方法
在这里，我们分析了不同的微阵列数据整合归一化方法，在定量模型反向工程的背景下。我们引入了两种基于现有归一化技术的预处理方法，并对归一化数据集进行了全面比较。
结论
结果确定了一种基于 Loess 归一化和迭代 K-均值组合的方法，该方法最适合此问题的时间序列归一化。
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