SpaNorm

Spatially aware library size normalisation, in R and Python

SpaNorm

Spatially aware library size normalisation for spatial transcriptomics, in R and Python.

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SpaNorm is a spatially aware library size normalisation method that removes library size effects from spatial molecular data while retaining biology. Standard single-cell inspired normalisation is not appropriate for spatial data, because library size is confounded with biology across space (Bhuva et al. 2024; Atta et al. 2024). SpaNorm models gene- and location-specific effects with smooth thin plate spline functions and keeps only the variation unrelated to library size.

Spatially aware

Gene- and location-specific effects are modelled with smooth thin plate spline functions, so only variation unrelated to library size is removed and biology is kept.

R and Python

The R package SpaNorm (Bioconductor) and the Python package spanormpy (on AnnData) share the same method and the same documentation, with R | Python tabs throughout.

Built to scale

Parallel workers, block-wise processing of disk-backed counts (R) and an optional GPU backend, with up to 2.7x faster spanorm on one H100 MIG slice (benchmark).

Install

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# release version
BiocManager::install("SpaNorm")
# development version from GitHub
BiocManager::install("BhuvaLab/SpaNorm")
pip install spanormpy
pip install git+https://github.com/BhuvaLab/spanormpy   # development version

For a GPU, install the CUDA build of PyTorch first (see https://pytorch.org), then use backend="gpu" or the default backend="auto". Optional extras: pip install "spanormpy[spatial]" for SpatialData input.

How it works

Diagram of the SpaNorm workflow

The SpaNorm workflow: SpaNorm takes gene expression and spatial coordinates as inputs. Using a gene-wise model (e.g., Negative Binomial), it decomposes spatially smooth variation into library size independent (biology) and library size related components. The adjusted data keep only the variation unrelated to library size.

Code examples throughout the site appear in R | Python tabs. Choosing a language in one tab switches every tab on the site, and the choice is remembered. See R vs Python for the name map and the intentional differences.

Cite

If you use SpaNorm or spanormpy, please cite:

Salim A, Bhuva DD, Chen C, Tan CW, Yang P, Davis MJ, Yang JYH. SpaNorm: spatially-aware normalization for spatial transcriptomics data. Genome Biology 2025, 26(1):109. doi:10.1186/s13059-025-03565-y

In R, citation("SpaNorm") prints this reference and a BibTeX entry.

Project

Authors

SpaNorm (R)

  • Dharmesh D. Bhuva, author and maintainer ORCID (d.bhuva@uq.edu.au)
  • Agus Salim, author ORCID
  • Ahmed Mohamed, author ORCID

spanormpy (Python)

  • Dharmesh D. Bhuva ORCID
  • Agus Salim ORCID

Downloads

Bioconductor total downloads PyPI downloads per month PyPI total downloads

Status

Bioconductor build (devel) R-CMD-check spanormpy CI Site build

Atta, Lyla, Kalen Clifton, Manjari Anant, Gohta Aihara, and Jean Fan. 2024. “Gene Count Normalization in Single-Cell Imaging-Based Spatially Resolved Transcriptomics.” bioRxiv, ahead of print. https://doi.org/10.1101/2023.08.30.555624.
Bhuva, Dharmesh D, Chin Wee Tan, Agus Salim, et al. 2024. “Library Size Confounds Biology in Spatial Transcriptomics Data.” Genome Biology 25 (1): 99.