pydoubletfinder 0.1.0

Pure-Python DoubletFinder — computational doublet detection in scRNA-seq via artificial-doublet pANN scoring, AnnData-native.

pydoubletfinder

A pure-Python re-implementation of DoubletFinder (McGinnis et al., Cell Systems 2019) for computational doublet detection in single-cell RNA-seq data.

• AnnData-native — drop-in for the scanpy ecosystem
• No rpy2, no R install — the full pN/pK sweep, bimodality coefficient, BCmvn, and pANN scoring are all implemented directly in NumPy/SciPy
• Same function surface as the R workflow (paramSweep → summarizeSweep → find.pK → doubletFinder)
• Bit-for-bit reproducibility against the R reference when fed matching PCA embeddings + artificial-doublet cell pairs (see tests/test_exact_match.py)

This is a standalone mirror of the canonical implementation that lives in omicverse (omicverse.single.DoubletFinder). All algorithmic work is developed upstream in omicverse and synced here for users who want DoubletFinder without the full omicverse stack.

Install

pip install pydoubletfinder

Quick-start (class API)

import anndata as ad
from pydoubletfinder import DoubletFinder

adata = ad.read_h5ad("mydata.h5ad")          # cells × genes, raw counts in .X

df = DoubletFinder(adata)

# 1) pN/pK parameter sweep
df.param_sweep(PCs=10)

# 2) Bimodality coefficient summary
df.summarize_sweep()

# 3) Optimal pK via BCmvn
bcmvn = df.find_pK()

# 4) Final scoring + classification
df.run(pN=0.25, nExp=round(0.075 * adata.n_obs))

adata.obs[[c for c in adata.obs.columns if c.startswith("DF.")]]

Low-level functional API (mirrors R one-to-one)

from pydoubletfinder import (
    param_sweep, summarize_sweep, find_pK,
    doublet_finder, model_homotypic,
    bimodality_coefficient,
)

# Per-real-cell pANN (needs a PCA embedding of [real + artificial] cells)
result = doublet_finder(
    pca_coord=my_pca,              # (n_real + n_doublets, n_PCs)
    n_real_cells=n_real,
    pN=0.25, pK=0.09, nExp=250,
)
result.pANN                          # np.ndarray
result.classifications               # {"Singlet", "Doublet"} per real cell
result.column_name_DF                # "DF.classifications_0.25_0.09_250"

# Homotypic-doublet proportion (match R modelHomotypic)
homotypic = model_homotypic(adata.obs["cluster"])

What's included

Python	R counterpart	Purpose
DoubletFinder class	—	AnnData-native lifecycle wrapper (like Milo, Monocle)
param_sweep	paramSweep	pN/pK sweep, one SweepEntry per (pN, pK)
summarize_sweep	summarizeSweep	bimodality coefficient per sweep entry, optional AUC
find_pK	find.pK	BCmvn + optimal-pK table
doublet_finder	doubletFinder	pANN + Doublet/Singlet classification
model_homotypic	modelHomotypic	homotypic-doublet proportion from cluster freqs
bimodality_coefficient, skewness, kurtosis	same	exported for direct use/testing
bkde, approxfun	KernSmooth::bkde, stats::approxfun	KernSmooth-compatible KDE + R approxfun
sample_artificial_doublets	internal	expose doublet-pair sampling for reproducibility

Reproducing R results exactly

The pipeline's randomness has two sources: which cell pairs become artificial doublets, and the PCA embedding of the merged matrix. To get identical outputs to an R run, provide both directly:

from pydoubletfinder import doublet_finder

result = doublet_finder(
    pca_coord=r_pca_embedding,      # from Seurat's reductions$[e-mail]
    n_real_cells=len(real_cells),
    pN=0.25, pK=0.09, nExp=250,
)

tests/test_exact_match.py runs the R reference (DoubletFinder::paramSweep + doubletFinder) inside the CMAP conda env, saves PCA coords and cell-pair indices, and checks that the Python port reproduces the pANN, BCreal, and classification vectors bit-for-bit.

Relationship to omicverse

Developed upstream in omicverse:

• Canonical implementation: omicverse.single.DoubletFinder
• Standalone mirror (this repo): same code, same API, minus the omicverse packaging

Citation

If you use this package, please cite the original DoubletFinder paper:

McGinnis, C.S., Murrow, L.M. & Gartner, Z.J. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems 8, 329–337 (2019).

and acknowledge omicverse / this repo for the Python port.

License

CC0 — matches the upstream R package.