Maxence Gélard

Single-cell annotation has long treated cell identity (lineage) and state (active transcriptional programs) with separate tools, while fixed-output classifiers can only return predefined types. CellScribe feeds frozen single-cell foundation-model embeddings into a pretrained language model through AdaLN adapters, so one shared model generates either an ontology-grounded cell type or a meta-program profile with activity scores under task-specific prompts. On the 309-type, 4.44M-cell study-holdout split of CELLxGENE, the full pipeline reaches 0.223 macro-F1 and 0.484 weighted-F1, above the 0.194 and 0.366 of a supervised probe on frozen scFoundation embeddings; on malignant cells from held-out 3CA studies it reaches 0.344 hallmark accuracy against the probe's 0.227.

Single-cell annotation has long treated cell identity (lineage) and state (active transcriptional programs) with separate tools, while fixed-output classifiers can only return predefined types. CellScribe feeds frozen single-cell foundation-model embeddings into a pretrained language model through AdaLN adapters, so one shared model generates either an ontology-grounded cell type or a meta-program profile with activity scores under task-specific prompts. On the 309-type, 4.44M-cell study-holdout split of CELLxGENE, the full pipeline reaches 0.223 macro-F1 and 0.484 weighted-F1, above the 0.194 and 0.366 of a supervised probe on frozen scFoundation embeddings; on malignant cells from held-out 3CA studies it reaches 0.344 hallmark accuracy against the probe's 0.227.