TransMap: Metric Embedding of Gene Relational Structures for Image-Native Single-Cell Learning

In single-cell omics, gene expression profiles lack canonical spatial coordinates, yet exhibit relational structure that can be conserved across species. We propose an image-native representation scheme that incorporates this structure into the input geometry for single-cell learning. The TransMap layout maps gene relationships onto a two-dimensional grid using Gromov–Wasserstein (GW) transport, with fused GW incorporating soft ortholog correspondences across species. Each selected gene occupies its own pixel, so its expression value is preserved exactly, while gene–gene distances are preserved approximately. We develop TransMap, a masked self-supervised model with a shared encoder, to learn from these images across species. Across five single-species datasets, the layouts achieve competitive distance and neighborhood preservation, while analysis across five species shows corresponding spatial organization of conserved modules. In a layout ablation across five datasets and two model sizes, GW layouts achieve lower mean masked patch reconstruction error than random layouts in all ten comparisons. Using unified human–mouse layouts constructed from training data alone, we pretrain a shared model on approximately 732K training metacells. On two of three human benchmarks, zero-shot scores fall within the range of evaluated foundation models; the same encoder also yields cell embeddings on two mouse datasets. These results support image-native representation as a promising basis for cross-species foundation models and motivate further study at larger data and model scales.