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Spatial-omics pipeline and analysis

sopa_logo

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, Molecular Cartography, and others. Sopa was designed for generability and low memory consumption on large images (scales to 1TB+ images).

Info

You may also be interested in Novae, developed by the same authors, now published in Nature Methods 🎉

Overview

The following illustration describes the main steps of sopa:

sopa_overview

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 cells
  • sopa 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!