stably: error-controlled stability selection for biomarker panel discovery
- 1The University of Manchester
Abstract
Motivation Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly popular for clinical proteomics due to its sensitivity and reproducibility. Univariate statistical analysis tools, such as limma and MSstats, are widely used for identifying differentially abundant proteins but cannot capture multivariate relationships between proteins that may provide greater discriminatory power as a panel. Machine learning approaches can address this gap, but typically prioritise predictive performance over feature stability, producing biomarker panels for downstream validation that vary depending on data splitting and are poorly suited to clinical translation. Results We developed stably, a Python package that implements stability selection with formal false positive control for DIA proteomics data. Using synthetic data with known ground-truth biomarkers, we show that the Shah and Samworth complementary pairs stability selection framework recovers more true synthetic biomarkers than the Meinshausen and Buhlmann framework at moderate effect sizes typical of proteomics (d = 0.5 - 2.0), while both maintain false positive rates well below their theoretical guarantees. Applied to a publicly available serum proteomics dataset from patients with all stages of pancreatic ductal adenocarcinoma (n=176), stably identified a stable 17-protein biomarker panel in the discovery cohort (n=120), which achieved higher predictive power (AUC = 0.93) in the validation cohort (n=56) than the panel selected by Byeon et al. (2024)(AUC 0.82). stably represents a principled, error-controlled method for biomarker panel discovery for translation into second cohorts. Availability and implementation stably is available on GitHub (https://github.com/byrnedaniel5-eng/stably); the version used in this study is archived on PyPI (https://pypi.org/project/stably/).
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BibTeX
@misc{byrne2026stably,
title = {{stably: error-controlled stability selection for biomarker panel discovery}},
author = {D. Byrne and M. McNamara and R. Unwin},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.11.750833},
url = {https://www.biorxiv.org/content/10.64898/2026.09.11.750833v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - stably: error-controlled stability selection for biomarker panel discovery
AU - D. Byrne
AU - M. McNamara
AU - R. Unwin
PY - 2026
DA - 2026/09/11
PB - bioRxiv
DO - 10.64898/2026.09.11.750833
UR - https://www.biorxiv.org/content/10.64898/2026.09.11.750833v1
AB - Motivation Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly popular for clinical proteomics due to its sensitivity and reproducibility. Univariate statistical analysis tools, such as limma and MSstats, are widely used for identifying differentially abundant proteins but cannot capture multivariate relationships between proteins that may provide greater discriminatory power as a panel. Machine learning approaches can address this gap, but typically prioritise predictive performance over feature stability, producing biomarker panels for downstream validation that vary depending on data splitting and are poorly suited to clinical translation. Results We developed stably, a Python package that implements stability selection with formal false positive control for DIA proteomics data. Using synthetic data with known ground-truth biomarkers, we show that the Shah and Samworth complementary pairs stability selection framework recovers more true synthetic biomarkers than the Meinshausen and Buhlmann framework at moderate effect sizes typical of proteomics (d = 0.5 - 2.0), while both maintain false positive rates well below their theoretical guarantees. Applied to a publicly available serum proteomics dataset from patients with all stages of pancreatic ductal adenocarcinoma (n=176), stably identified a stable 17-protein biomarker panel in the discovery cohort (n=120), which achieved higher predictive power (AUC = 0.93) in the validation cohort (n=56) than the panel selected by Byeon et al. (2024)(AUC 0.82). stably represents a principled, error-controlled method for biomarker panel discovery for translation into second cohorts. Availability and implementation stably is available on GitHub (https://github.com/byrnedaniel5-eng/stably); the version used in this study is archived on PyPI (https://pypi.org/project/stably/).
ER -