scPathVQ: A Pathway-Guided Discrete Latent Interface for Single-Cell Understanding and Generation

Effective single-cell transcriptomic representations are fundamental to modeling cellular states in virtual cells. Existing methods typically tokenize individual genes, but are inherently constrained by the high-dimensional, sparse, and unordered nature of transcriptomic inputs. Moreover, cell embeddings are often obtained by flattening and pooling latent sequences, which can obscure the functional gene modules that collectively shape cellular states. We introduce **scPathVQ**, a pathway-guided discrete latent interface that provides a compact representation while preserving functional gene-module structure for both cellular-state understanding and generation. Its biology-structured tokenizer organizes transcriptome-wide genes into pathway-induced units that capture functional gene modules, complemented by highly variable genes that provide cell-specific expression context. A latent bottleneck compresses these contextualized features into a fixed-length sequence. Cell-state vector quantization maps each latent component to a shared codebook. The resulting quantized sequence preserves functional gene-module structure for perturbation response modeling, while gated pooling yields a compact cell level representation for downstream understanding tasks. We pretrain scPathVQ on 34 million human cells and evaluate several downstream tasks. The pooled representation supports strong transfer across cell-level tasks, while the quantized sequence enables accurate perturbation-response prediction. Generated expression profiles recover gene pathway responses and associated functional cell-state patterns. Ablation studies further identify the contribution of pathway-guided organization to the learned representation.