Adaptive Feature Propagation with Reciprocal Neighbors for Single-Cell Imputation

Graph-based feature propagation (FP) provides an efficient, non-parametric approach to single-cell RNA sequencing (scRNA-seq) imputation. However, severe sparsity distorts cell neighbor rankings and causes over-smoothing when using static graphs and fixed propagation depths. We propose scRFP, a training-free FP framework that adapts neighbor selection, propagation depth, and graph degree without requiring cell-type labels. Rather than using reciprocal cell pairs directly as sparse graph edges, scRFP employs them to derive a closed-form metric transformation that downweights directions that vary within reciprocal pairs and recalibrates neighbor similarities across all cells. We then monitor neighborhood contrast to stop hard propagation, and prune low-confidence edges to accommodate varying cellular densities before soft diffusion. Across seven benchmark scRNA-seq datasets with a single configuration, scRFP achieves the best clustering performance on six, improving ARI by up to 12.1% (relative) over the strongest baseline, and the lowest technical-dropout recovery error in most settings, at runtime comparable to existing FP methods; removing any of the three adaptations lowers average ARI.