AmbiDose

AmbiDose removes ambient RNA from droplet scRNA-seq. Empty droplets estimate the soup profile χ. Each cell receives an operational dose d_c = ρ_c · n_c. Type-aware subtraction writes non-negative integer counts. The method does not correct batch effects or assign cell types.

Docs: Read the Docs.

Install

pip install ambidose
# or: conda install -c conda-forge -c bioconda 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

Treat rho as an operational dose. Check obs["ambidose_rho_trust"] and the QC report before using it as a contamination rate.

Status

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

Next steps

Citation

Pin the package version used in the analysis, for example ambidose==0.3.2. Software record: 10.5281/zenodo.22278199. 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 = {0.3.2},
  doi     = {10.5281/zenodo.22278199},
  url     = {https://doi.org/10.5281/zenodo.22278199},
}

License

Software: Apache License 2.0.

Author

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

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0.5.1

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ambidose 0.3.2

AmbiDose

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