Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
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DOI：
10.1093/bioinformatics/btg313
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发表时间：
2003-11-22
期刊：
影响因子：
5.8
通讯作者：
Husmeier, D
中科院分区：
文献类型：
--
作者：
Husmeier, D
动机：贝叶斯网络已被应用于从微阵列基因表达数据中推断基因调控相互作用。这一推断问题尤其困难，因为数百个基因之间的相互作用必须从非常小的数据集中学习，这些数据集通常只包含一个细胞周期中的几十个时间点。大多数先前的研究都是通过将预测的基因调控相互作用与生物学文献中已知的相互作用进行比较来评估对真实基因表达数据的推断结果。由于缺乏已知的黄金标准，这种方法是有争议的，这使得对灵敏度和特异度的估计，即真和(互补)假检测率，不可靠和困难。本研究的目的是在真实的模拟研究中测试贝叶斯网络范式的可行性。首先，从一个真实的生物网络中模拟基因表达数据，该网络包括DNA、mRNAs、非活性蛋白质单体和活性蛋白质二聚体。然后利用贝叶斯网络和马尔可夫链蒙特卡罗的贝叶斯学习方法，以逆向工程的方法从这些数据中推断出交互作用网络。这允许估计恢复的真实交互作用的特定目标比例所引起的虚假基因交互作用的比例。结果表明，网络推理性能如何随训练集的大小、先前假设的不足程度、实验采样策略和进一步的基于序列的信息的包含而变化。
Motivation: Bayesian networks have been applied to infer genetic regulatory interactions from microarray gene expression data. This inference problem is particularly hard in that interactions between hundreds of genes have to be learned from very small data sets, typically containing only a few dozen time points during a cell cycle. Most previous studies have assessed the inference results on real gene expression data by comparing predicted genetic regulatory interactions with those known from the biological literature. This approach is controversial due to the absence of known gold standards, which renders the estimation of the sensitivity and specificity, that is, the true and (complementary) false detection rate, unreliable and difficult. The objective of the present study is to test the viability of the Bayesian network paradigm in a realistic simulation study. First, gene expression data are simulated from a realistic biological network involving DNAs, mRNAs, inactive protein monomers and active protein dimers. Then, interaction networks are inferred from these data in a reverse engineering approach, using Bayesian networks and Bayesian learning with Markov chain Monte Carlo.Results: The simulation results are presented as receiver operator characteristics curves. This allows estimating the proportion of spurious gene interactions incurred for a specified target proportion of recovered true interactions. The findings demonstrate how the network inference performance varies with the training set size, the degree of inadequacy of prior assumptions, the experimental sampling strategy and the inclusion of further, sequence-based information.