RankFuse: Integrating single-cell representations through neighbour rankings

No single-cell foundation model is best on every dataset, yet their embeddings live in incompatible coordinate systems, so their complementary strengths cannot be pooled. We present RankFuse, an encoder-agnostic method that combines frozen single-cell foundation models through what they agree on: each cell's ranked nearest neighbours across batches. Reciprocal rank fusion merges the per-encoder rankings into a single graph, and a one-layer graph convolution learns an integrated representation for batch integration, with a variance term to prevent collapse and a batch adversary to remove batch effects. No encoder is fine-tuned, so the adapter trains a small fraction of the parameters of any contributing model and new encoders can be added at the cost of a neighbour search. On PBMC and an immune cell atlas, fusing scGPT, Geneformer, a batch-adversarial scVI and a DINO encoder, both pretrained on a 46-study corpus, improves the scIB benchmark score over the strongest single encoder by up to 5.6% relative, and yields the strongest batch correction on the atlas. Ablations show that fusion beats a single-encoder graph with the same adapter, and that performance depends far more on which encoders build the graph than on which supply node features, suggesting that neighbour-rank agreement is a coordinate-free interface for combining representation models, of which single-cell encoders are one instance.