Automated calibration for stability selection in penalised regression and graphical models

Barbara Bodinier; Sarah Filippi; Therese Haugdahl Nøst; Julien Chiquet; Marc Chadeau-Hyam

doi:10.1093/jrsssc/qlad058

Automated calibration for stability selection in penalised regression and graphical models

J R Stat Soc Ser C Appl Stat. 2023 Jul 13;72(5):1375-1393. doi: 10.1093/jrsssc/qlad058. eCollection 2023 Nov.

Authors

Barbara Bodinier¹, Sarah Filippi², Therese Haugdahl Nøst³, Julien Chiquet⁴, Marc Chadeau-Hyam¹

Affiliations

¹ Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
² Department of Mathematics, Imperial College London, London, UK.
³ Department of Community Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway, NO-9037 Tromsø, Norway.
⁴ Université Paris-Saclay, AgroParisTech INRAE, UMR MIA, SolsTIS team, Paris, France.

Abstract

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

Keywords: OMICs integration; calibration; graphical model; penalised model; stability selection.

Abstract

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