Skip to main content

scFair

PyPI version PyPI downloads Bioconda Conda downloads Python versions Documentation Status CI License DOI bioRxiv

scFair selects highly variable genes (HVGs) for single-cell RNA-seq. It handles two choices most pipelines already make, plus an optional population count:

Choice Common default What scFair does
How many genes (n)? Fixed 2000 n_top_genes="auto" (default) estimates a base size from multi-seed density structure. Pass an int when a protocol already fixes n. Auto is slower than a fixed k.
Hard top-k cutoff Rank globally and keep top-k Default append keeps the global top-k and adds the next append_budget genes from the same ranking (top-(k+m)). Use balance_method="none" for exact top-k.

Optional: estimate_n_populations counts populations from a 3D density field after sc.pp.neighbors. It works best on well-separated data with up to about 20 populations.

Docs: Read the Docs.

What you need

  • Python 3.10+ (tested 3.10–3.12)
  • An AnnData object with raw integer counts in .X or layers["counts"]

Install

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

Details: installation guide.

First run

import scanpy as sc
import scfair as scf

# raw integer counts in .X or layers["counts"]
scf.pp.highly_variable_genes(adata)
# annotates adata.var["highly_variable"]; keep the full gene matrix
# (scanpy PCA uses that mask by default — no manual subset step)

sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.leiden(adata, flavor="igraph", n_iterations=2, directed=False)

Default path: structure auto-n + same-rank append. Auto runs extra multi-seed graph builds and is slower than a fixed k. Read adata.uns["scfair"]["hvg"]["auto_message"] for a one-line summary of the chosen base size. Fixed length for a locked protocol:

scf.pp.highly_variable_genes(adata, n_top_genes=2000)

Pure top-k (no append tail):

scf.pp.highly_variable_genes(adata, n_top_genes=2000, balance_method="none")

Population count after neighbors:

sc.pp.neighbors(adata)
est = scf.pp.estimate_n_populations(adata)
print(est.n_populations, est.confidence)

Status

0.10.0 (Beta). Import as import scfair as scf and use names in scfair.__all__ and scf.pp. See the API reference.

Next steps

  1. Installation
  2. Quickstart
  3. Tutorials
  4. FAQ if something looks off

Citation

If you use scFair in published work, please cite the preprint:

Zhao Li, Aaron James, Shengxuan Li.
scFair: Geometry-Aware Gene Budgets and Same-Rank Extension for Highly Variable Gene Selection.
bioRxiv (2026). https://doi.org/10.64898/2026.08.08.743679

@article{li2026scfair,
  title   = {scFair: Geometry-Aware Gene Budgets and Same-Rank Extension for Highly Variable Gene Selection},
  author  = {Li, Zhao and James, Aaron and Li, Shengxuan},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.08.08.743679},
  url     = {https://www.biorxiv.org/content/10.64898/2026.08.08.743679v1},
}

For the software record, cite the Zenodo DOI. Pin the package version used in the analysis, for example scfair==0.10.0. See CITATION.cff.

License

Software: Apache License 2.0.

Author

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

Metadata

Release files for scfair 0.10.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for scfair 0.10.0
File Size Uploaded
scfair-0.10.0.tar.gz 1.5 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for scfair 0.10.0
File Interpreter ABI Platform
scfair-0.10.0-py3-none-any.whl Python 3 none any Details

Total release size: 1.6 MB

Release files / scfair-0.10.0.tar.gz

Download URL scfair-0.10.0.tar.gz
Size 1.5 MB
Tags Source
SHA-256 checksum
How to use checksums
b106624733d094343af07e672b69c54dffea77af2ed12e9aec3e0ea3e26214ba
BLAKE2b-256 checksum
How to use checksums
a3f99df0072e390eef7cb1840b3915d48b0cfc250c73f6afe532b8dfe440e916
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 15, 2026.

Transparency log

Release files / scfair-0.10.0-py3-none-any.whl

Download URL scfair-0.10.0-py3-none-any.whl
Size 70.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
e10397ac8a47300ac455a776c31e29bb7b0e7c71e9d57614e790643c58452526
BLAKE2b-256 checksum
How to use checksums
035ce5a3afb2c17a3aeaed017cb077bd80f0b62f613345afef8767b020bdeba4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 15, 2026.

Transparency log

Release history Release notifications | RSS feed

0.10.2

2 release files

0.10.1

2 release files

This release

0.10.0 This release

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page