BACKGROUND Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms. RESULTS In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse. CONCLUSIONS Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

中文翻译：

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利用遗传扰动对基因调控网络进行贝叶斯差分分析。


背景技术基因调控网络（GRN）可以从基因表达数据和遗传扰动中推断出来。在不同条件下，同一基因集的基因数据可能会有所不同，从而导致GRN不同。检测不同条件下GRN之间的结构差异对于理解基因功能和生物学机制具有重要意义。结果在本文中，我们提出了一种贝叶斯融合算法来联合推断两种不同条件下 GRN 的差分结构。该算法是为使用结构方程模型（SEM）建模的GRN而开发的，这使得将遗传扰动纳入模型中以提高推理精度成为可能，因此我们将其命名为BFDSEM。与单独推断成对 GRN 并识别与推断的 GRN 之间的差异的朴素方法不同，我们首先重新参数化两个 SEM 以形成充分利用两组基因数据的集成模型，然后求解通过遵循分离 GRN 和差分 GRN 都是稀疏的标准，开发一种新颖的贝叶斯融合先验来重新参数化模型。结论 对合成数据进行计算机模拟，将 BFDSEM 与两种最先进的联合推理算法：FSSEM 和 ReDNet 进行比较。结果表明，BFDSEM 的性能与 FSSEM 相当，并且总体优于 ReDNet。 BFDSEM算法也应用于肺癌及癌旁正常组织的真实数据集，得到的正常GRN和差异GRN与以往文献报道的结果一致。附加文件 1 中免费提供了实现 BFDSEM 的开源程序。