Objective:

To develop a computational method for gene selection from mRNA-seq data that captures the complexity of transcriptional states better than conventional approaches, which often struggle with log2 fold-change and False Discovery Rate thresholds.

Approach:

• Method Development: The Cartesian Distance-Based Gene Expression (CDBGE) selector was created to identify differentially expressed genes using multidimensional expression distances.
• Application: The CDBGE selector was applied to macrophage polarization states, trained on publicly available macrophage transcriptomic datasets, and validated with in vitro human macrophages stimulated with IFN−γ/LPS, conditioned medium from HepG2 liver cancer cells, or IL10, as well as human embryonic stem cell differentiation datasets.

Key Findings:

• The CDBGE selector identified subtype-specific markers more effectively than standard differential expression pipelines, particularly in the context of macrophage polarization.
• The method revealed dynamic transcriptional transitions over time in macrophage polarization, highlighting its ability to capture complex biological processes.

Interpretation:

Distance-based gene selection provides an improved strategy for analyzing complex mRNA-seq datasets, as demonstrated by the findings.

Conclusion:

The CDBGE selector is a robust, scalable, and broadly applicable tool for differential gene expression analysis and phenotype characterization across various biological systems.