A network-based gene-weighting approach for pathway analysis
A network-based gene-weighting approach for pathway analysis
复制标题
用于路径分析的基于网络的基因加权方法
DOI：
10.1038/cr.2011.149
复制
发表时间：
2012-03-01
期刊：
影响因子：
44.1
通讯作者：
Ji, Hongbin
中科院分区：
文献类型：
--
作者：
Fang, Zhaoyuan;Tian, Weidong;Ji, Hongbin
经典的算法，旨在确定生物学途径显着相关的研究条件，经常减少途径的基因集，与一个明显的无知的组成不等价的各种基因在一个定义的途径。我们在这里设计了一个基于网络的方法来确定这种不等价的基因权重。所确定的基因权重在生物学上是一致的，并且对网络扰动具有鲁棒性。通过将基因权重整合到经典的基因集分析中，并随后校正与多亚基蛋白质相关的“过度计数”偏差，我们开发了一种新的基因加权通路分析方法，如在R软件包中实现的，称为“基于基因关联网络的通路分析”（GANPA）。通过分析几个微阵列数据集，包括p53数据集，哮喘数据集和三个乳腺癌数据集，我们证明了我们的方法是生物学上可靠的和可重复的，因此有助于微阵列数据解释和假设生成。
Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.