Identification of differentially expressed subnetworks based on multivariate ANOVA.
Identification of differentially expressed subnetworks based on multivariate ANOVA.
复制标题
DOI：
10.1186/1471-2105-10-128
复制
发表时间：
2009-04-30
影响因子：
3
通讯作者：
Park T
中科院分区：
文献类型：
--
作者：
Hwang T;Park T
自从高通量蛋白质-蛋白质相互作用(PPI)数据最近对人类可用以来，将PPI数据与其他基因组范围的数据相结合的兴趣越来越大。特别是，利用基因表达数据识别与表型相关的PPI亚网络一直是人们非常关注的问题。为了成功地识别重要的子网络，需要使用具有适当评分方法的搜索算法。在这里，我们提出了一种基于多变量方差分析(Manova)的评分方法，并通过贪婪搜索来识别差异表达的PPI子网络。给定基于Manova的评分方法，我们执行贪婪搜索以确定PPI网络中得分最高的子网络。我们的方法被成功地应用于人类微阵列数据集。每个已识别的子网络都被用基因本体论(GO)术语注释，从而产生与表型相关的功能途径或复合体。我们还将这些结果与其他评分方法如t统计量和基于互信息的评分方法的结果进行了比较。与其他方法相比，基于马诺瓦的方法产生了含有更多蛋白质的子网络。此外，基于Manova的方法确定的子网络往往由高度相关的蛋白质组成。本文提出了一种基于Manova的评分方法，利用贪婪搜索将PPI数据与表情数据相结合。建议将此方法用于对大型子网进行高度敏感的检测。
Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks. Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins. This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.
登录
查看更多内容
通过途径依赖性建模癌症进展。
影响因子：
4.3
作者：
Edelman, Elena J.;Guinney, Justin;Chi, Jen-Tsan;Febbo, Phillip G.;Mukherjee, Sayan
通讯作者：
Mukherjee, Sayan
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
1.7
作者：
Newton, MA;Kendziorski, CM;Tsui, KW
通讯作者：
Tsui, KW
DOI：
10.1073/pnas.0506577102
发表时间：
2005-09-20
影响因子：
11.1
作者：
Tian, L;Greenberg, SA;Park, PJ
通讯作者：
Park, PJ
影响因子：
9.9
作者：
通讯作者：
--