sopa 2.1.11
Spatial-omics pipeline and analysis
Requires Python: >=3.10

<p align="center">
  <img src="https://raw.githubusercontent.com/gustaveroussy/sopa/main/docs/assets/sopa.png" alt="sopa_logo" width="250"/>
</p>
<p align="center"><b><i>
	Spatial omics pipeline and analysis
</b></i></p>

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Built on top of [SpatialData](https://github.com/scverse/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).

> [!NOTE]
> You may also be interested in [Novae](https://github.com/MICS-Lab/novae), developed by the same authors, now published in [Nature Methods](https://www.nature.com/articles/s41592-025-02899-6) 🎉

## Documentation

Check [Sopa's documentation](https://gustaveroussy.github.io/sopa) to get started. It contains installation explanations, CLI/API details, and tutorials.

## Overview

The following illustration describes the main steps of `sopa`:

<p align="center">
  <img src="https://raw.githubusercontent.com/gustaveroussy/sopa/main/docs/assets/overview_white.png" alt="sopa_overview" width="100%"/>
</p>

## Installation

Sopa can be installed from `PyPI` on all OS, for any Python version from `3.10` to `3.13` (included). On a new environment, run the following command:
```sh
pip install sopa
```

> [!NOTE]
> See this [installation section](https://gustaveroussy.github.io/sopa/getting_started/) for more details about extras and other installations modes.

## Features
Sopa comes in four different flavours, each corresponding to a different use case:
- `API`: use directly `sopa` as a Python package for complete flexibility and customization.
- `Snakemake pipeline`: choose a config, and run our pipeline on your spatial data in a couple of minutes.
- `nf-core/sopa`: run Sopa with Nextflow (see [this repo](https://github.com/nf-core/sopa) and the corresponding [usage guide](https://nf-co.re/sopa/usage)). Great for Docker users.
- `CLI`: use our command-line-interface for prototyping quickly your own pipeline (advanced users).

### API

Below is a minimal example of API usage. For a complete API description, please refer to the [documentation](https://gustaveroussy.github.io/sopa/tutorials/api_usage/).

```python
import sopa

sdata = sopa.io.xenium("path/to/data") # reading Xenium data

sopa.make_image_patches(sdata) # creating overlapping patches
sopa.segmentation.cellpose(sdata, "DAPI", diameter=30) # running cellpose segmentation
sopa.aggregate(sdata) # counting the transcripts inside the cells
```

### Snakemake pipeline

Clone our repository, choose a config [here](https://github.com/gustaveroussy/sopa/tree/main/workflow/config) (or create your own), and execute our pipeline locally or on a high-performance cluster:
```bash
git clone https://github.com/gustaveroussy/sopa.git
cd sopa/workflow
snakemake --configfile=/path/to/yaml_config --config data_path=/path/to/data_directory --cores 1 --use-conda
```

For more details on `snakemake` configuration and how to properly setup your environments, please refer to the [documentation](https://gustaveroussy.github.io/sopa/tutorials/snakemake/).

### CLI

Below are examples of commands that can be run with the `sopa` CLI. For a complete description of the CLI, please refer to the [documentation](https://gustaveroussy.github.io/sopa/tutorials/cli_usage/).

```bash
> sopa --help # show command names and arguments
> sopa convert merscope_directory --technology merscope # read some data
> sopa patchify image merscope_directory.zarr # make patches for low-memory segmentation
> sopa segmentation cellpose merscope_directory.zarr --diameter 60 --channels DAPI # segmentation
> sopa resolve cellpose merscope_directory.zarr # resolve segmentation conflicts at boundaries
> sopa aggregate merscope_directory.zarr --average-intensities # transcripts/channels aggregation
> sopa explorer write merscope_directory.zarr # convert for interactive vizualisation
```

## Cite us
Our article is published in [Nature Communications](https://www.nature.com/articles/s41467-024-48981-z). You can cite Sopa as below:

```txt
Blampey, Q., Mulder, K., Gardet, M. et al. Sopa: a technology-invariant pipeline for analyses of image-based spatial omics.
Nat Commun 15, 4981 (2024). https://doi.org/10.1038/s41467-024-48981-z
```