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
0.5.3 (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==0.5.3.
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 = {0.5.3},
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
1 maintainer
Metadata
1 maintainer
Release files for ambidose 0.5.3
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| File | Size | Uploaded | |
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|---|---|---|---|---|
| ambidose-0.5.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 614.4 kB
Release files / ambidose-0.5.3.tar.gz
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