Spatial-omics pipeline and analysis
Built on top of SpatialData, Sopa enables processing and analyses of spatial omics data with single-cell resolution (spatial transcriptomics or multiplex imaging data) using a standard data structure and output. We currently support the following technologies: Xenium, Visium HD, MERSCOPE, CosMX, PhenoCycler, MACSima, Hyperion. Sopa was designed for generability and low memory consumption on large images (scales to 1TB+
images).
🎉 sopa==2.0.0
is out! It introduces many new API features; check our migration guide to smoothly update your code base.
Overview
The following illustration describes the main steps of sopa
:
Xenium Explorer is a registered trademark of 10x Genomics. The Xenium Explorer is licensed for usage on Xenium data (more details here).
Why use sopa
Sopa is a modern Python toolkit that is easy to use and offers many advantages:
sopa
is designed to be memory-efficient, and it scales to slides with millions of cellssopa
can be used on any spatial technology with single-cell resolution, making it straightforward to apply it to multiple projects- Many segmentation tools are implemented in Sopa, so you can try/compare them all easily
- Depending on your need, you can use our API, CLI, or directly the Snakemake pipeline
- You can visualize your data in an interactive manner
- Spatial operations are optimized and use
shapely
internally sopa
integrates naturally with other community tools such as Scanpy or Squidpy.
Start using Sopa by reading our getting started guide!