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Simulating gene silencing through intervention analysis

Author

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  • Vera Djordjilović
  • Monica Chiogna
  • Chiara Romualdi

Abstract

We propose a novel method for simulating the effects of gene silencing. Our approach combines relevant subject matter information provided by biological pathways with gene expression levels measured in regular conditions to predict the behaviour of the system after one of the genes has been silenced. We achieve this by modelling gene silencing as an external intervention in a causal graphical model. To account for the uncertainty that is associated with the structure learning of the graphical model, we adopt a bootstrap approach. We illustrate our proposal on a Drosophila melanogaster gene silencing experiment.

Suggested Citation

  • Vera Djordjilović & Monica Chiogna & Chiara Romualdi, 2020. "Simulating gene silencing through intervention analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 887-907, August.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:4:p:887-907
    DOI: 10.1111/rssc.12412
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    References listed on IDEAS

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    3. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    4. Hyunghoon Cho & Bonnie Berger & Jian Peng, 2016. "Reconstructing Causal Biological Networks through Active Learning," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
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