Causal discovery algorithms aim to identify causal relations from observational data and have become a popular tool for analysing genetic regulatory systems. In this work, we applied causal discovery to obtain novel insights into the genetic regulation underlying head‐and‐neck squamous cell carcinoma. Some methodological challenges needed to be resolved first. The available data contained missing values, but most approaches to causal discovery require complete data. Hence, we propose a new procedure combining constraint‐based causal discovery with multiple imputation. This is based on using Rubin's rules for pooling tests of conditional independence. A second challenge was that causal discovery relies on strong assumptions and can be rather unstable. To assess the robustness of our results, we supplemented our investigation with sensitivity analyses, including a non‐parametric bootstrap to quantify the variability of the estimated causal structures. We applied these methods to investigate how the high mobility group AT‐Hook 2 (HMGA2) gene is incorporated in the protein 53 signalling pathway playing an important role in head‐and‐neck squamous cell carcinoma. Our results were quite stable and found direct associations between HMGA2 and other relevant proteins, but they did not provide clear support for the claim that HMGA2 itself is a key regulator gene.

中文翻译：

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数据不完整的因果关系发现

因果发现算法旨在从观测数据中识别因果关系，并已成为分析遗传调控系统的流行工具。在这项工作中，我们应用因果发现来获得有关头颈部鳞状细胞癌的遗传调控的新见解。首先要解决一些方法上的挑战。可用数据包含缺失值，但是大多数因果发现方法需要完整的数据。因此，我们提出了一种将基于约束的因果发现与多重归因相结合的新过程。这是基于使用鲁宾规则来合并条件独立性测试的。第二个挑战是因果发现依赖于强有力的假设并且可能相当不稳定。为了评估结果的可靠性，我们通过敏感性分析（包括非参数引导程序）对我们的研究进行了补充，以量化估计的因果结构的变异性。我们应用这些方法研究了高迁移率族AT-Hook 2（HMGA2）基因如何整合到在头颈鳞状细胞癌中起重要作用的53蛋白信号通路中。我们的结果相当稳定，并且发现HMGA2与其他相关蛋白之间存在直接关联，但并未为HMGA2本身是关键调节基因的说法提供明确支持。我们应用这些方法研究了高迁移率族AT-Hook 2（HMGA2）基因如何整合到在头颈鳞状细胞癌中起重要作用的53蛋白信号通路中。我们的结果相当稳定，并且发现HMGA2与其他相关蛋白之间存在直接关联，但它们并未为HMGA2本身是关键调节基因的说法提供明确支持。我们应用这些方法研究了高迁移率族AT-Hook 2（HMGA2）基因如何整合到在头颈鳞状细胞癌中起重要作用的53蛋白信号通路中。我们的结果相当稳定，并且发现HMGA2与其他相关蛋白之间存在直接关联，但并未为HMGA2本身是关键调节基因这一说法提供明确支持。