spatialtis 0.5.0

Ultra-fast spatial analysis toolkit for large-scale spatial single-cell data

SpatialTis

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

🔋 Highlighted spatial analysis

• 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

📦 Other analysis

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

Quick Start

Installation

pypi

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

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.

Low level API

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