py-SpaNorm

Python port of the R/Bioconductor package SpaNorm — spatially-aware normalisation for spatial transcriptomics data.

Install

pip install -e .

Quickstart

import anndata as ad
from spanorm import SpaNorm

# Load your spatial data (AnnData format)
# adata.obsm['spatial'] should contain spatial coordinates
# adata.layers['counts'] or adata.X should contain raw counts
adata = ad.read_h5ad("your_data.h5ad")

# Class-based API
sn = SpaNorm(adata)
sn.normalize(sample_p=0.25, df_tps=6, verbose=True)
sn.find_svgs()
sn.pca(n_svgs=3000, n_components=50)

# Access results
logcounts = sn.adata.layers['logcounts']     # Normalized data
svg_results = sn.adata.var[['svg_F', 'svg_p', 'svg_fdr']]  # SVG results
pca_coords = sn.adata.obsm['PCA']            # PCA coordinates

Functional API (R one-to-one mirror)

from spanorm import spanorm, spanorm_svg, spanorm_pca, filter_genes, fast_size_factors

# Filter genes
keep = filter_genes(counts, prop=0.1)

# Normalize
adata = spanorm(adata, sample_p=0.25, df_tps=6)

# Find SVGs
adata = spanorm_svg(adata)

# PCA
adata = spanorm_pca(adata, n_svgs=3000, n_components=50)

Python ⇄ R Function Map

Algorithm

SpaNorm works by:

1. Fitting a spatial regression model using thin-plate spline bases for spatial coordinates
2. Modeling library size effects separately from biological signal
3. Normalizing data using log-PAC, Pearson residuals, or mean/median biology methods
4. Identifying spatially variable genes via likelihood ratio tests

License

GPL-3.0-or-later (matching upstream R package)

Citation

Bhuva DD, Salim A, Mohamed A. SpaNorm: Spatially-aware normalisation for spatial transcriptomics data. Bioconductor.