scGANT: Cell-Conditioned Biological Guidance for Single-Cell Foundation Models

Single-cell foundation models learn transferable cellular representations from large-scale transcriptomic atlases (e.g. scRNA-seq). Most of these Transformer-based foundation models represent each cell as a sequence of gene tokens and rely on data-driven attention to infer gene dependencies from raw data. However, this principle is fundamentally mismatched to single-cell biology, where gene dependencies are biologically structured and cell-state specific. To model such cell-specific gene dependencies, we introduce scGANT, a novel single-cell foundation model framework with cell-conditioned biological guidance. scGANT includes two key innovations: (i) Cell-conditioned prior induction is designed to adapt multi-channel biological priors (including transcription-factor regulation, pathway co-membership, co-expression, and chromosomal proximity) to the current cellular context. (ii) The resulting cell-conditioned prior signals are then incorporated into the biologically guided attention, which determines which gene pairs are allowed to communicate and how strongly their interactions are weighted. Across five benchmark datasets spanning cell-level and gene-level tasks, scGANT improves macro-F1 on cell type annotation by up to 12.2%, achieves the SOTA frozen-encoder batch-integration overall scIB score, and the best performance on all four differential-expression metrics over 22 held-out perturbation predictions. In an additional case study, scGANT identifies a 12-gene Leiden cluster containing five genes with no direct prior edges among them. Published studies suggest that these genes are involved in related processes. This illustrates scGANT's hypothesis generation beyond direct prior-edge copying. Collectively, these results support biological guidance of gene-token communication as a promising design principle for single-cell foundation models. Code is available at: https://anonymous.4open.science/r/scGANT-1BFB.