2023/07/13 by Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nøst +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Statistical Methods and Inference
paper · pdf · doi:10.1093/jrsssc/qlad058
crossref issued 2023/07/13 · crossref published 2023/07/13 · crossref published-online 2023/07/13 · openalex publication_date 2023/07/13 · crossref created 2023/07/13 · crossref published-print 2023/12/22 · crossref deposited 2023/12/23 · openalex created_date 2025/10/10 · crossref indexed 2026/07/29 · openalex updated_date 2026/07/30
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.