Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes

Abstract

Gene regulatory network inference is a key approach for elucidating molecular mechanisms underlying complex diseases, but accurately inferring them from high-dimensional data, especially when sample sizes are imbalanced, remains a significant challenge. Although the L 1 -type regularization methods have been used for gene network inference, the existing methods often fail under conditions involving high dimensionality, noise, and unequal sample sizes across phenotypes. To overcome these limitations, this study developed netRL, a novel computational framework that integrates the Random Lasso with prior network biological knowledge. The proposed method leveraged a bootstrap-based strategy to stabilize the selection of key regulatory genes and incorporates network-informed penalization using centrality measures (i.e., hubness and betweenness centrality). This study also introduced a statistical strategy using a hypergeometric test to assess the significance of the inferred edges, thereby enhancing the reliability of the network. Through extensive simulation studies, this study demonstrated that netRL outperforms conventional methods in both network estimation and gene selection. Applying netRL to whole-blood RNA-seq profiles from the Japan COVID-19 Task Force, this study successfully identified distinct phenotype-specific molecular interplays between asymptomatic and critical cases despite pronounced sample imbalance. The findings reveal that asymptomatic networks were dense and enriched for ribosomal proteins, whereas critical networks were sparse, centralized, and characterized by hub genes such as NFKBIA, B2M, CXCL8, and FOS. Pathway enrichment further revealed phenotype-specific biological processes, highlighting molecular signatures of disease progression. The results of this study suggest that enhancing the activity of asymptomatic condition-specific markers (e.g., ribosomal proteins) may provide important insights into the molecular mechanisms underlying COVID-19 severity. Collectively, these results demonstrate that netRL enables biologically interpretable and statistically robust network inference, offering new insights into the molecular basis of COVID-19 severity and broader applications in systems biology.