Cross-species transcriptome analysis using Gromov-Wasserstein optimal transport

Sequence homology underpins cross-species analysis but cannot identify evolutionarily distinct genes that play analogous regulatory roles. Furthermore, ethical restrictions on human experiments necessitate analytical frameworks that translate insights from other animals to humans. To address these challenges, we developed Species-OT, a cross-species transcriptome analysis framework based on Gromov-Wasserstein optimal transport, which quantitatively compares the geometry of transcriptome distributions. Given a pair of bulk or single-cell RNA-sequencing datasets, Species-OT returns a gene-to-gene correspondence capturing probabilistic alignments of regulatory roles, and a transcriptomic distance quantifying overall divergence. Applied pairwise, Species-OT yields a transcriptomic discrepancy array and a hierarchical clustering tree analogous to a phylogenetic tree. We validated Species-OT using bulk RNA-seq data from human, mouse, and macaque germ cell specification as well as scRNA-seq data from pluripotent stem cells of six mammalian species. Species-OT identified evolutionarily related and distinct gene correspondences including biologically unexplored candidates, while transcriptomic discrepancies recapitulated expected species relationships. This is joint work with T. Nakamura, K. Fujiwara, M. Imamura, M. Nagano, M. Saitou, Y. Imoto, and Y. Hiraoka.