AmbiDose

AmbiDose removes ambient RNA from droplet scRNA-seq. Empty droplets estimate the soup profile χ; each cell gets an operational dose d_c = ρ_c · n_c. Type-aware subtraction writes non-negative integer counts. It does not integrate batches or annotate cell types.

Docs: Read the Docs.

Install

pip install ambidose

Python 3.10–3.13. A GPU is not required. Details: installation.

First run

import scanpy as sc
import ambidose as amdose

adata = sc.read_10x_mtx("filtered_feature_bc_matrix/")
adata = amdose.denoise(adata, raw="raw_feature_bc_matrix.h5", sample_key=None)
# adata.X is denoised integer UMI; original counts in layers["raw_counts"]

ambidose denoise --input /path/to/outs --output cleaned.h5ad

rho is an operational dose, not a calibrated contamination rate. Inspect obs["ambidose_rho_trust"] and the QC report before using it quantitatively.

Status

0.3.0 (Alpha). Import as import ambidose as amdose. The entry point is denoise(). See the API reference.

Next steps

Installation
Quickstart
Tutorials
FAQ if something looks off
From R (reticulate or CLI)

Citation

Pin the package version used in the analysis, for example ambidose==0.3.0. See CITATION.cff and the citation page.

@software{li2026ambidose,
  title   = {AmbiDose: per-cell ambient dose removal for droplet scRNA-seq},
  author  = {Li, Zhao},
  year    = {2026},
  version = {0.3.0},
  url     = {https://github.com/leelieber2025/AmbiDose},
}

License

Software: Apache License 2.0.

Author

Zhao Li (李钊) Email: leelieber@gmail.com

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ambidose 0.3.0

AmbiDose

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