Bootstrap-enhanced regularization addressing multicollinearity and skewness in high-dimensional immunophenotyping data

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Abstract Background Accurate identification and estimation of variables associated with outcomes or disease states are critical for advancing diagnosis, prognosis, and precision medicine in biomedical research. Regularized regression techniques, such as lasso, are widely employed to enhance interpretability by reducing model complexity and identifying significant variables. However, these methods face two major challenges: (1) the exclusion of important variables due to high correlation with included predictors, and (2) the presence of skewness in human biomedical datasets, which violates key statistical assumptions. Current approaches that fail to address these issues simultaneously may lead to biased interpretations and unreliable coefficient estimates. To overcome these limitations, we propose an enhanced two-step approach, the Bootstrap-Enhanced Regularization Method (BERM). Results BERM outperformed existing regularization methods in variable selection, achieving the highest overall balanced accuracy while maintaining competitive coefficient estimation performance across a range of simulated sparsity, noise, and dimensionality scenarios. We further demonstrated the effectiveness of BERM by applying it to a human immunophenotyping dataset to identify important immune parameters in the autoimmune disease, type 1 diabetes. Conclusion BERM is a robust approach for variable selection and coefficient estimation in complex biomedical datasets. Its consistent performance across a wide range of data conditions supports more reliable identification of important variables. An open-source implementation of BERM is available as an R package on GitHub ( https://github.com/xiaorudong/berm ).

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