scFair

scFair is drop-in HVG selection for single-cell RNA-seq that targets two everyday pain points:

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

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.pp.scale(adata, max_value=10)
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.leiden(adata)

That is the default path: pick a base size from the data (n_top_genes="auto"), then append a few more genes from the same ranking. Auto is slower than a fixed k (extra multi-seed graph builds). Read adata.uns["scfair"]["hvg"]["auto_message"] for a one-line plain summary of the chosen base size. For a classical fixed list or a paper protocol:

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

To match scanpy with no extension:

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

Status

0.7.0 (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. Tutorial: PBMC 10k misclustered cells vs standard HVG
4. FAQ if something looks off

License

Software: Apache License 2.0.

Author

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