ABSORB: Absorbing-State Discrete Diffusion for Single-Cell Gene Expression Imputation

Single-cell RNA sequencing produces matrices that are highly sparse, yet a large fraction of zero entries are missing: either they are silent in the gene expression or are lost during transcript capture. This ambiguity makes imputation a selective missing-data problem in contrast to a generic denoising problem. We introduce ABSORB, an absorbing-state discrete diffusion process that represent gene expressions using a dedicated absorbing [MASK] state. Our proposed framework tokenizes the expression into per-gene equal-mass quantile bins, and learns to recover masked observed entries from the remaining expression profile. During inference, measured entries remain fixed while candidate dropouts are replaced with a [MASK] and reconstructed by a role-conditioned Transformer. The model predicts categorical distributions for each gene rather than a conditional mean, ensuring non-negativity is held by construction. To distinguish genuine recovery of missing expression from the sparse input, we evaluate only against known truth and external labels. Across a four-level simulated dataset spanning 50–72% dropout, ABSORB achieves Spearman correlations of $0.90$–$0.94$ and log-scale RMSE of $0.55$–$0.61$, outperforming nine baselines. Additionally, we evaluate cell-type clustering as a label-assisted downstream analysis, while separating it from the independent evidence provided by held-out gene expression recovery.