Automated calibration for stability selection in penalised regression and graphical models

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 (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.

Main paper 21 pages, SI: 17 pages

References in corpus (1)

• Estimation of Gaussian graphs by model selection

Cited by in corpus (2)

• Automated calibration of consensus weighted distance-based clustering approaches using sharp
• Bayesian Stability Selection and Inference on Selection Probabilities