Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.
Bioinformatics. 2013 Dec 15;29(24):3167-73. doi: 10.1093/bioinformatics/btt557. Epub 2013 Sep 26.
The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.
We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.
Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.
Source code for the method is available upon request.
许多转录本的丰度会因各种分子和环境扰动而发生显著变化。在这种情况下，一个关键问题是：是什么中间分子扰动导致了观察到的转录变化？调控程序不仅受转录变化的控制，还受蛋白质丰度和翻译后修饰的控制，这使得从数据中进行直接因果推断变得困难。然而，过去几十年的生物医学研究已经揭示了大量的因果信号通路，可以用来识别能够解释特定转录变化集的良好候选者。
我们采用贝叶斯方法将基因表达谱与从先前生物学知识构建的分子相互作用因果图进行整合。此外，我们通过相应的医学主题词来定义特定相互作用的生物学背景。可以查询贝叶斯网络以建议可以与改变的表达谱因果相关的上游调节剂。
我们的方法将优先考虑处于正确生物学背景的候选调节剂，能够分层探索由此产生的假设，并考虑完整的因果关系网络，以找到最佳的上游调节剂集。我们在不同的生物学数据集上展示了我们方法的强大功能，即地塞米松治疗反应、干细胞分化和神经病理性疼痛模型。在所有情况下，都可以验证相关的生物学见解。
可根据要求提供该方法的源代码。
