Automated calibration for stability selection in penalised regression and graphical models

TL;DR

This paper presents an automated calibration method for stability selection in penalised regression and graphical models, improving feature selection in high-dimensional data with known block structures, demonstrated through simulations and real biological data.

Contribution

It introduces an automated calibration procedure for stability selection that maximizes a stability score and accounts for block structures, applicable to LASSO and graphical models.

Findings

Outperforms existing calibration methods in simulations

Identifies a novel role of LRRN3 in smoking response

Implemented in the R package sharp

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.