spatialtis 0.5.0rc0

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

SpatialTis requires python version >= 3.8

pypi

Install the basics

pip install spatialtis

For the full features

pip install 'spatialtis[all]'

Install the current development version

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

Docker (Not Available)

The quickest way to run is to use a docker image, it contains all you need to start from cell type identification.

docker pull spatialtis/spatialtis

To run a jupyter notebook from the docker image and mount your data folder to it:

cd your/data/
docker run -it [--rm] -p 8888:8888
  --mount type=bind,source="$PWD",target=/work \
  spatialtis/spatialtis
# if port 8888 is taken, try `-p 9999:8888` and change to 9999

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.