Software

We develop open-source computational methods to understand biological heterogeneity in disease, using single-cell, spatial and multi-omics data. Everything we release is open source, documented and free to use.

Software

This software is part of the approved de.NBI service Spatial Transcriptomics Toolbox. Please help us improve by taking our short user survey.

Tools we lead

SSAM Segmentation-free cell-type mapping for imaging-based spatial transcriptomics. Park et al., Nature Communications (2021). AGPL-3.0
SSAM-lite Lightweight SSAM implementation that runs in the browser (ssam-lite.netlify.app). Tiesmeyer et al., Frontiers in Genetics (2022). MIT
Sainsc Segmentation-free spatial analysis at scale for sequencing- and imaging-based spatial transcriptomics. Müller-Bötticher et al., Small Methods (2025). MIT
SpatialLeiden Spatially-aware clustering via multiplex modularity optimisation. Müller-Bötticher et al., Genome Biology (2025). GPL-3.0
multiSPAETI Dimensionality reduction that maximises the product of variance and Moran's I. Müller-Bötticher et al., Genome Biology (2025). MIT
ovrl.py Detection of cell overlap and vertical signal integrity in imaging-based SRT. Tiesmeyer et al., Nature Biotechnology (2026). MIT
clrmappy Automatically colours high-dimensional data using dimensionality reduction. MIT
SACCELERATOR Framework to run and compare spatial clustering tools. Sun et al., Nature Methods (2026). MIT-0

Tools we contribute to

Spatial2Galaxy Galaxy platform for reproducible spatial transcriptomics processing (ELIXIR).

This software is part of the approved de.NBI service Spatial Transcriptomics Toolbox. Please help us improve by taking our short user survey.