Causal Inference in Biology Networks with Integrated Belief Propagation
Causal Inference in Biology Networks with Integrated Belief Propagation
复制标题
具有集成置信传播的生物网络中的因果推理
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
E. Schadt
中科院分区：
文献类型：
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作者：
Rui Chang;Jonathan R. Karr;E. Schadt
推断分子和高阶表型之间的因果关系是阐明生命系统复杂性的关键步骤。在这里，我们提出了一种新的方法来推断因果关系，不再受条件依赖参数，限制了统计因果推理方法的能力，以解决因果关系的图形模型，是马尔可夫等价集的约束。我们的方法利用贝叶斯置信传播来推断给定假设图结构的扰动事件对分子特征的响应。的推断的响应分布和观察到的数据之间的距离测量被定义为评估的假设的因果关系的“健身”。为了测试我们的算法，我们推断基因网络的等价类内的因果关系，其中可能的功能相互作用的形式被假定为非线性的，给定的合成微阵列和RNA测序数据。我们也将我们的方法应用到真实的具有v-结构和反馈环的代谢网络中。我们表明，我们的方法可以概括的因果结构和恢复的反馈回路，只有从稳态数据，传统的方法不能。
Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.