Husmeier Dirk
Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Edinburgh, EH9 3JZ, UK.
Bioinformatics. 2003 Nov 22;19(17):2271-82. doi: 10.1093/bioinformatics/btg313.
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.
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.
The programs and data used in the present study are available from http://www.bioss.sari.ac.uk/~dirk/Supplements
贝叶斯网络已被应用于从微阵列基因表达数据推断基因调控相互作用。这个推断问题特别困难，因为必须从非常小的数据集中学习数百个基因之间的相互作用，这些数据集通常在一个细胞周期中只包含几十个时间点。以前的大多数研究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，来评估真实基因表达数据的推断结果。由于缺乏已知的金标准，这种方法存在争议，这使得灵敏度和特异性（即真检测率和（互补的）假检测率）的估计不可靠且困难。本研究的目的是在一个现实的模拟研究中测试贝叶斯网络范式的可行性。首先，从一个涉及DNA、mRNA、无活性蛋白质单体和活性蛋白质二聚体的现实生物网络中模拟基因表达数据。然后，使用贝叶斯网络和马尔可夫链蒙特卡罗贝叶斯学习，以逆向工程方法从这些数据中推断相互作用网络。
模拟结果以接收者操作特征曲线表示。这允许估计在指定的恢复真实相互作用的目标比例下产生的虚假基因相互作用的比例。研究结果表明了网络推断性能如何随训练集大小、先验假设的不充分程度、实验采样策略以及是否包含进一步的基于序列的信息而变化。
本研究中使用的程序和数据可从http://www.bioss.sari.ac.uk/~dirk/Supplements获取。
