Inferring signaling pathways with probabilistic programming.
Inferring signaling pathways with probabilistic programming.
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DOI：
10.1093/bioinformatics/btaa861
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发表时间：
2020-12-30
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影响因子：
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通讯作者：
Gitter A
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文献类型：
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作者：
Merrell D;Gitter A
细胞通过令人眼花缭乱的复杂生化过程来调节自己，这种生化过程被称为信号传导途径。这些通常被描绘成一个网络，其中节点代表蛋白质，边缘表示它们彼此之间的影响。为了在细胞水平上理解疾病和治疗，准确理解起作用的信号通路至关重要。由于信号通路可因疾病而改变，因此从病情或患者特异性数据推断信号通路的能力是非常有价值的。存在各种各样的技术来推断信号通路。我们以过去的工作为基础，将信号通路推断制定为磷酸化蛋白质组学时间过程数据的动态贝叶斯网络结构估计问题。我们采用贝叶斯方法，使用马尔可夫链蒙特卡罗估计可能的动态贝叶斯网络结构的后验分布。我们的主要贡献是：(i)一种新颖的提议分布，可以有效地对稀疏图进行采样；（ii）放松常见的限制性建模假设。我们在Julia中使用Gen概率编程语言实现了我们的方法，名为稀疏信号路径采样。概率规划是构建统计模型的强大方法。生成的代码是模块化的、可扩展的、易读的。特别是Gen语言，允许我们为生物图定制推理程序，并确保有效的采样。我们在模拟数据和HPN-DREAM路径重建挑战上评估了我们的算法，并将我们的性能与各种基线方法进行了比较。我们的结果证明了概率规划的巨大潜力，特别是Gen在生物网络推理方面的巨大潜力。在https://github.com/gitter-lab/ssps上找到完整的代码库。补充数据可在生物信息学网站获得。
Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. Find the full codebase at https://github.com/gitter-lab/ssps. Supplementary data are available at Bioinformatics online.