Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
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DOI：
10.1038/s41598-017-08582-x
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发表时间：
2017-10-05
影响因子：
4.6
通讯作者：
Tsamardinos, Ioannis
中科院分区：
文献类型：
--
作者：
Triantafillou, Sofia;Lagani, Vincenzo;Tsamardinos, Ioannis
了解定义分子系统的因果关系使我们能够预测系统将如何响应不同的干预措施。区分因果关系和单纯的关联通常需要随机实验。存在从有限实验中自动发现因果关系的方法，但迄今为止很少在系统生物学应用中进行测试。在这项工作中，我们将最先进的因果发现方法应用于大量公共大规模细胞计数数据集，测量人类免疫系统的细胞内信号蛋白及其对几种扰动的反应。我们展示了如何使用不同的实验条件来促进因果发现，并应用两种基本方法来产生特定于上下文的因果预测。因果预测在两项不同研究的独立数据集中是可重复的，但通常与 KEGG 通路数据库不一致。在此背景下，我们讨论了自动化因果发现需要克服的注意事项，以成为系统生物学常规数据分析的一部分。
Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intracellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.