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Stability selection represents an attractive approach to identify sparse setsof features jointly associated with an outcome in high-dimensional contexts. Weintroduce an automated calibration procedure via maximisation of an in-housestability score and accommodating a priori-known block structure (e.g.multi-OMIC) data. It applies to (LASSO) penalised regression and graphicalmodels. Simulations show our approach outperforms non-stability-based andstability selection approaches using the original calibration. Application ofmulti-block graphical LASSO on real (epigenetic and transcriptomic) data fromthe Norwegian Women and Cancer study reveals a central/credible and novelcross-OMIC role of LRRN3 in the biological response to smoking. Proposedapproaches were implemented in the R package sharp.

Automated calibration for stability selection in penalised regression and graphical models