Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230096
Authors: 
Foraita, Ronja
Friemel, Juliane
Günther, Kathrin
Behrens, Thomas
Bullerdiek, Jörn
Nimzyk, Rolf
Ahrens, Wolfgang
Didelez, Vanessa
Year of Publication: 
2020
Citation: 
[Journal:] Journal of the Royal Statistical Society: Series A (Statistics in Society) [ISSN:] 1467-985X [Volume:] 183 [Issue:] 4 [Pages:] 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.
Subjects: 
Gene expression
Graphical models
Head‐and‐neck squamous cell carcinoma
HMGA2 gene
Human papilloma‐virus
PC algorithm
Protein 53 signalling pathway
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version

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