Causal discovery of gene regulation with incomplete data

Ronja Foraita, Juliane Friemel, Kathrin Günther, Thomas Behrens, Jörn Bullerdiek, Rolf Nimzyk, Wolfgang Ahrens and Vanessa Didelez

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 4, 1747-1775

Abstract: 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.

Date: 2020
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