Inferring gene regulatory networks by ANOVA
Inferring gene regulatory networks by ANOVA
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DOI：
10.1093/bioinformatics/bts143
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发表时间：
2012-05-15
期刊：
影响因子：
5.8
通讯作者：
Zimmer, Ralf
中科院分区：
文献类型：
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作者：
Kueffner, Robert;Petri, Tobias;Zimmer, Ralf
结果：我们提出了一个新的网络推理得分，eta(2)，这是方差分析得出的。候选转录因子：靶基因（TF:TG）的关系被认为更有可能，如果TF和TG的表达是相互依赖的至少在所检查的实验的一个子集。我们通过非参数的非线性相关系数eta(2)来评估这种依赖性。它快速，易于应用，并且不需要对输入数据进行离散化。在最近的DREAM5盲评估中，可以说是对推理方法最全面的评估，我们基于eta(2)的方法在真实表达纲要上被评为最佳表现。它也比最近发表的其他比较评估中测试的方法表现得更好。根据对DREAM5进行的qPCR实验估计，我们预测的新预测中约有一半是真实的相互作用。结论：得分eta(2)具有许多有趣的特征，可以有效地检测基因调控相互作用。对于大多数实验设置，它是一个有趣的替代其他依赖度量，如皮尔森相关性或互信息。
Results: We present a new score for network inference, eta(2), that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by eta(2), a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on eta(2) was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5.Conclusions: The score eta(2) has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearson's correlation or mutual information.