基于t检验和逐步网络搜索的有向基因调控网络推断算法

Directed gene regulatory network inference algorithm based on t-test and stepwise network search

为了克服基于条件互信息的路径一致算法(PCA-CMI)无法识别调控方向的缺陷,并进一步提高网络推断准确率,提出了一种基于t检验和逐步网络搜索的有向网络推断算法(DNI-T-SRS)。首先,对不同实验条件下的表达数据进行t检验以辨别基因调控的上下游关系,指导路径一致(Path Consensus)算法中条件基因的选取,根据CMI2(Conditional Mutual Inclusive Information)剔除网络中的冗余边,得到了基于t检验的有向调控关系推断算法CMI2NI-T(CMI2-based Network Inference guided by t-Test);然后,建立有向调控关系对应的米氏微分方程模型对数据进行拟合,根据贝叶斯信息准则进行逐步网络搜索以修正网络推断结果。利用CMI2NI-T推断DREAM6挑战中的两个测试网络,所得到的曲线下面积(AUC)分别为0.7679和0.9796,相较于PCA-CMI分别提高了16.23%和11.62%;通过进一步的数据拟合后DNI-T-SRS的推断准确率分别达到了86.67%和100.00%,相较于PCA-CMI分别提高了18.19%和10.52%。实验结果表明,所提DNI-T-SRS算法能够有效剔除间接调控关系并保留直接调控连接,得到精确的基因调控网络推断结果。

In order to overcome the shortage that the Path Consensus Algorithm based on Conditional Mutual Information(PCA-CMI)cannot identify the regulation direction and further improve the accuracy of network inference,a Directed Network Inference algorithm enhanced by t-Test and Stepwise Regulation Search(DNI-T-SRS)was proposed.First,the upstream and downstream relationships of genes were identified by a t-test performed on the expression data with different perturbation settings,by which the conditional genes were selected for guiding Path Consensus(PC)algorithm and calculating Conditional Mutual Inclusive Information(CMI2)to remove redundant regulations,and an algorithm named CMI2-based network inference guided by t-Test(CMI2NI-T)was developed.Then,the corresponding Michaelis-Menten differential equation model was established to fit the expression data,and the network inference result was further corrected by a stepwise network search based on Bayesian information criterion.Numerical experiments were conducted on two benchmark networks of the DREAM6 challenge,and the Area Under Curves(AUCs)of CMI2NI-T were 0.7679 and 0.9796,which were 16.23%and 11.62%higher than those of PCA-CMI.With the help of additional process of data fitting,the DNI-T-SRS achieved the inference accuracies of 86.67%and 100.00%,which were 18.19%and 10.52%higher than those of PCA-CMI.The experimental results demonstrate that the proposed DNI-T-SRS can eliminate indirect regulatory relationships and preserve direct regulatory connections,which contributes to precise inference results of gene regulatory networks.