Geometric inference of cross-species transcriptome correspondence using Gromov-Wasserstein optimal transport
Sequence homology reveals evolutionary gene correspondences, but identifying functionally corresponding genes within specific cell types or during cell fate specification remains non-trivial. Here, frameworks that translate insights from model organisms into other species would accelerate wet-lab studies, so we develop Species-OT, a cross-species transcriptome analysis framework using 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 validate Species-OT using bulk RNA-seq data for cell fate specification from pluripotent stem cells in mice, monkeys, and humans and scRNA-seq data from pluripotent stem cells of six mammalian species. Species-OT identifies gene correspondences within cell fate specification, while transcriptomic discrepancies recapitulate expected species relationships.