SpatialTis is an ultra-fast spatial analysis toolkit for large-scale spatial single-cell data.

• ✔️ Spatial Transcriptome (Non single-cell)
• ✔️ Spatial Proteome (Single-cell)
• 🦀 Core algorithms implements in Rust
• 🚀 Parallel processing support

• Cell neighbors search (KD-Tree/R-Tree/Delaunay)
• Cell-Cell Interaction
• Marker spatial co-expression
• Spatial variable genes (current support: SOMDE)
• GCNG: Inferring ligand-receptor using graph convolution network
• Identify neighbor dependent markers

• Spatial distribution
• Hotspot detection
• Spatial auto-correlation
• Spatial heterogeneity

Quick Start

SpatialTis requires Python >= 3.8.

pip install spatialtis

# For full features
pip install 'spatialtis[all]'

Install the current development version

pip install git+https://github.com/Mr-Milk/SpatialTis.git

docker pull mrmilk/spatialtis

To start a docker container:

cd your/data/
docker run -it --rm -p 8888:8888 -v "${PWD}:/analysis" spatialtis

• -it: Run the container in interactive mode
• -rm: Clean file system in container after shutting down
• If local port 8888 is taken, try -p 9999:8888 and change to 9999.
• -v: Mount your data directory to the working directory /analysis in the container. ${PWD} is the directory where you run this command. All changes made in this directory will be saved.

If you are interested in using low level algorithms yourself, Please refer to spatialtis_core It provides clear document for all exposed API.