AmbiDose: per-cell removal budgets for ambient RNA contamination

Abstract

Ambient RNA contamination confounds expression analysis in droplet-based single-cell and single-nucleus RNA sequencing. Here we present AmbiDose, which combines an ambient profile estimated from empty droplets with cell-specific nominal removal budgets and expression-based protection of lineage-enriched genes. Its ambient dose guides count removal rather than estimating a calibrated contamination fraction. In a held-out human-mouse GEM-X mixture, AmbiDose removed 92.0% of cross-species UMIs while retaining 99.6% of same-species UMIs. It reduced off-target marker expression in kidney-tumor and fetal-liver cohorts and yielded the lowest proximal-tubule marker leakage among the tested methods across five strain-mixed kidney libraries. Simulations supported the contribution of cell-specific dose estimates but identified reduced endogenous retention or incomplete removal when ambient and endogenous expression overlapped. AmbiDose is available as a Python package compatible with Scanpy.