py-spotsweeper 0.1.0

Spatially-aware quality control for spatial transcriptomics

py-SpotSweeper

Spatially-aware quality control for spatial transcriptomics.

Python port of R/Bioconductor package SpotSweeper v1.5.0.

Install

pip install spotsweeper

Quick start

import anndata as ad
from spotsweeper import local_variance, local_outliers, find_artifacts, flag_visium_outliers

# Load your spatial data as AnnData with adata.obsm['spatial']
adata = ad.read_h5ad("your_data.h5ad")

# 1. Compute local variance of mitochondrial percentage
adata = local_variance(adata, metric="subsets_Mito_percent", n_neighbors=36)

# 2. Detect local outliers in library size
adata = local_outliers(adata, metric="sum", direction="lower", log=True)

# 3. Flag systematic Visium outliers
adata = flag_visium_outliers(adata)

# 4. Find artifacts (single sample only)
adata = find_artifacts(adata, mito_percent="subsets_Mito_percent", n_order=5)

Functions

Function	Description
local_variance()	Local variance of QC metrics using kNN + robust regression
local_outliers()	Outlier detection using MAD-based modified z-scores
find_artifacts()	Artifact detection via multi-scale variance + PCA + k-means
flag_visium_outliers()	Flag known systematic Visium outlier spots
plot_qc_metrics()	Spatial scatter plot of QC metrics
plot_qc_pdf()	Multi-page PDF of QC plots per sample

Speed vs R

Function	R	Python	Speedup
localVariance	8.9s	0.5s	19x
localOutliers	1.3s	0.2s	8x
findArtifacts	16s	5.5s	3x
flagVisiumOutliers	0.05s	0.003s	14x

Parity

All functions pass parity gates against R reference outputs:

Function	Metric	Value	Gate
localVariance	max abs err	2.67e-6	1e-5
localOutliers	F1	1.0	0.95
findArtifacts	ARI	1.0	0.95
flagVisiumOutliers	exact match	1.0	1.0

Requirements

• Python >= 3.9
• numpy, scipy, pandas, anndata, scikit-learn, matplotlib

License

MIT

References

Totty et al. (2025) "SpotSweeper: spatially-aware quality control for spatial transcriptomics." Bioconductor.