Automated calibration for stability selection in penalised regression and graphical models
提出一种自动校准方法，通过最大化稳定性分数并考虑已知的块结构（如多组学数据），用于LASSO惩罚回归和图模型中的稳定性选择。模拟显示优于传统方法，应用于挪威女性与癌症研究的表观遗传和转录组数据，发现LRRN3在吸烟生物反应中的跨组学作用。
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.