An information-theoretic metric learning approach for cross-species transcriptome integration: Identifying sex differentiation transcriptomic structure between protogynous fishes and mouse

Sexual differentiation is a fundamental process conserved across animals; nonetheless, cross-species comparisons remain limited. Here, I applied information-theoretic metric learning (ITML) to project mouse testis RNA-seq data into a three-dimensional space trained on protogynous fish transcriptomes, using stage-to-stage expression change geometry rather than gene identity or annotation. ITML was anchored with dmrt1 and amh , revealing conserved clustering of known sex differentiation genes (e.g., bmp8b and stra8 ). Rather than serving as a direct differentiation model, this framework provides a structural reference for evaluating whether transcriptomic changes align with conserved developmental trajectories, offering a framework for identifying novel candidates in mammalian sex differentiation.