scWEAVE: An Efficient Multi-Species Single-Cell Foundation Model via Meta-Gene Compression and Contrastive Alignment

Single-cell foundation models (scFMs) show great promise across various biological tasks. However, most existing models are computationally expensive and trained mainly on human data, limiting their applicability to multi-species analysis. Extending scFMs across species is non-trivial: gene sets differ substantially across species, and biological divergence together with batch effects induce significant cross-species distribution shift. We present scWEAVE, an efficient scFM that jointly reduces computational cost and generalizes across species. We introduce a gene compression module that uses knowledge from protein language models and gene ontology to softly compress genes from any species into a shared, compact set of meta-genes, making cross-species data directly comparable while shortening the token sequence the transformer must process. To address distribution shift, scWEAVE decomposes each cell's total variation into a shared cell factor encoding shared cell-state variation and a remnant factor that supplies species-specific information needed for accurate masked expression prediction – the main objective of scFMs. We further align the shared cell factor across species using a supervised contrastive regularizer based on cell-ontology identity, which accounts for inconsistent label granularity between species-specific annotations and corrects for residual batch-effect distortion during pretraining. We validate scWEAVE through extensive experiments on datasets spanning multiple species. scWEAVE matches or exceeds baseline performance on ortholog retrieval, gene expression imputation, batch effect removal, and novel cell type discovery, while cutting inference time up to 35x and memory up to 6x relative to TranscriptFormer.