EDDY: a novel statistical gene set test method to detect differential genetic dependencies.
EDDY: a novel statistical gene set test method to detect differential genetic dependencies.
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DOI：
10.1093/nar/gku099
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发表时间：
2014-04
影响因子：
14.9
通讯作者：
Kim S
中科院分区：
文献类型：
--
作者：
Jung S;Kim S
识别条件之间的差异特征是理解特定生物过程的分子特征及其机制的流行方法。尽管已经提出了许多用于鉴定基因或基因集的差异表达的测试，但是由于其计算复杂性，在开发用于条件之间的基因的差异相互作用的方法方面的成功有限。我们提出了一种依赖差异性评价（EDDY）的方法，这是一个统计测试一组基因之间的差异依赖两个条件。与以往的方法集中在个别基因的差异表达或个别基因-基因相互作用的相关性变化，EDDY比较两种情况下，通过评估依赖网络的概率分布从基因。该方法已被评估，并通过模拟研究与其他方法进行比较，并应用于多形性胶质母细胞瘤数据导致信息丰富的癌症和多形性胶质母细胞瘤亚型相关的结果。与基于差异表达的方法基因集富集分析的比较表明，EDDY鉴定的基因集与基因集富集分析鉴定的基因集互补。EDDY还显示出比基因集共表达分析低得多的假阳性，基因集共表达分析是一种基于个体基因-基因相互作用的相关性变化的方法，因此提供了更多信息的结果。该算法的Java实现对非商业用户免费提供。下载网址：http://biocomputing.tgen.org/software/EDDY。
Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene–gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene–gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from: http://biocomputing.tgen.org/software/EDDY.
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影响因子：
3
作者：
Hwang T;Park T
通讯作者：
Park T
解剖小鼠乳腺肿瘤中细胞过程失调的动力学。
影响因子：
4.4
作者：
Mentzen WI;Floris M;de la Fuente A
通讯作者：
de la Fuente A
影响因子：
3.5
作者：
Leonardson AS;Zhu J;Chen Y;Wang K;Lamb JR;Reitman M;Emilsson V;Schadt EE
通讯作者：
Schadt EE
影响因子：
1.7
作者：
Shojaie, Ali;Michailidis, George
通讯作者：
Michailidis, George
DOI：
10.1073/pnas.0409522102
发表时间：
2005-02-08
影响因子：
11.1
作者：
Sharan, R;Suthram, S;Ideker, T
通讯作者：
Ideker, T