  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 (LASSO) penalised regression and graphical
models. Simulations show our approach outperforms non-stability-based and
stability selection approaches using the original calibration. Application of
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.
