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Structured Detection of Interactions with the Directed Lasso

Author

Listed:
  • Hristina Pashova

    (University of Washington
    Axio Research)

  • Michael LeBlanc

    (Fred Hutchinson Cancer Research Center)

  • Charles Kooperberg

    (Fred Hutchinson Cancer Research Center)

Abstract

When considering low-dimensional gene–treatment or gene–environment interactions, we might suspect groups of genes to interact with treatment or environment in a similar way. For example, genes associated with related biological processes might interact with an environmental factor or a clinical treatment in its effect on a phenotype correspondingly. We use the idea of a structured interaction model together with penalized regression to limit the model complexity in a model in which we believe the interactions might behave in a similar way. We propose the directed lasso, a regression modeling strategy using a pairwise fused lasso penalty to encourage interaction model simplicity through fusion of effect size. We compare the performance of the directed lasso to the lasso and other methods in a simulation study and on data sampled from a breast cancer clinical trial.

Suggested Citation

  • Hristina Pashova & Michael LeBlanc & Charles Kooperberg, 2017. "Structured Detection of Interactions with the Directed Lasso," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 676-691, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9184-6
    DOI: 10.1007/s12561-016-9184-6
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    References listed on IDEAS

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