A versatile distance-based approach for gene expression selection across diverse biological systems.

Introduction: Differential gene expression analysis is essential for characterizing immune cell phenotypes, yet conventional approaches-typically based on log Methods: To address this limitation, we developed a new computational method for gene selection from mRNA-seq data: the Cartesian Distance-Based Gene Expression (CDBGE) selector. This algorithm identifies differentially expressed genes by leveraging multidimensional expression distances rather than relying on traditional univariate statistical cutoffs, enabling a more refined and biologically coherent gene-marker selection. Results: We applied the CDBGE selector to construct a gene-based framework for distinguishing macrophage polarization states. The model was trained using publicly available macrophage transcriptomic datasets and subsequently validated with Discussion: These findings demonstrate that distance-based gene selection provides an improved strategy for analyzing complex mRNA-seq datasets. Overall, the CDBGE selector offers a robust, scalable, and broadly applicable tool for differential gene expression analysis and phenotype characterization.