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Built on top of [SpatialData](https://github.com/scverse/spatialdata), Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and low-memory consumption on large images (scales to `1TB+` images).
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Built on top of [SpatialData](https://github.com/scverse/spatialdata), Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and lowmemory consumption on large images (scales to `1TB+` images).
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The pipeline outputs contain: (i) Xenium Explorer files for interactive visualization, (ii) a HTML report for quick quality controls, and (iii) a SpatialData `.zarr` directory for further analyses.
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The pipeline outputs contain: (i) Xenium Explorer files for interactive visualization, (ii) an HTML report for quick quality controls, and (iii) a SpatialData `.zarr` directory for further analyses.
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# Documentation
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The easiest way to getting started with `sopa` is to check [our documentation](https://gustaveroussy.github.io/sopa). It contains installation explainations, CLI/API details, and usages examples.
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The easiest way to start with `sopa` is to check [our documentation](https://gustaveroussy.github.io/sopa). It contains installation explanations, CLI/API details, and tutorials.
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# Overview
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# Installation
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### PyPI installation
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Sopa can be installed via `PyPI` on all operating system. Make sure you have an environment with `python==3.10` (more versions will be soon supported), and run the following command:
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Sopa can be installed via `PyPI` on all operating systems. Make sure you have an environment with `python==3.10` (more versions will be soon supported), and run the following command:
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```
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pip install sopa
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```
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To install extras (for example if you want to use `snakemake`/`cellpose`/`baysor`/`tangram`), please run:
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To install extras (for example, if you want to use `snakemake`/`cellpose`/`baysor`/`tangram`), please run:
If you don't know in which order to run these commands, refer to the image in the [homepage](..), or see our [CLI usage tutorial](../tutorials/cli_usage).
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When running the `sopa` CLI, some arguments are required while some are optional. For instance, for the `sopa read` command, `sdata_path` is an argument and a path has to be given directly, while `technology` is an option, and in this case the `--technology` prefix has to be used. For instance, if you read MERSCOPE data, it will be:
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When running the `sopa` CLI, some arguments are required, while some are optional. For instance, for the `sopa read` command, `sdata_path` is an argument, and a path has to be given directly, while `technology` is an option (in this case, the `--technology` prefix has to be used). For instance, if you read MERSCOPE data, it will be:
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- One or multiple image(s), usually corresponding to one or multiple `.tiff` file(s)
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- Optionally a file of transcript location, usually a `.csv` or `.parquet` file
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- Optionally, a file of transcript location, usually a `.csv` or `.parquet` file
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In our tutorials, we use `data_path` to denote the path to your raw data. Select the right tab below to understand what is the right path to your raw data:
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Our tutorialsuse `data_path` to denote the path to your raw data. Select the correct tab below to understand what is the right path to your raw data:
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=== "Xenium"
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`data_path` is the directory containing the following files: `morphology.ome.tif` and `transcripts.parquet`
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=== "Hyperion"
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`data_path` is the directory containing multiple `.ome.tiff` files (one file per channel)
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## Cellpose is not segmenting enough cells, what should I do?
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## Cellpose is not segmenting enough cells; what should I do?
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- Maybe `min_area` is too high, and all the cells are filtered because they are smaller than this area. Remind that, when using Cellpose, the areas correspond to pixels^2.
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- This can be due to a low image quality. If the image is too pixelated, consider increasing `gaussian_sigma` (e.g., `2`) under the cellpose parameters of our config. If the image has a low contrast, consider increasing `clip_limit` (e.g., `0.3`). These parameters are detailed in [this example config](https://github.com/gustaveroussy/sopa/blob/master/workflow/config/example_commented.yaml).
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- Consider updating the official Cellpose parameters. In particular, try `cellprob_threshold=-6` and `flow_threshold=2`.
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## Can I use Nextflow instead of Snakemake?
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Nextflow is not supported yet, but we are working on it. If you want, you can also help re-writing our Snakemake pipeline for Nextflow.
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Nextflow is not supported yet, but we are working on it. You can also help re-write our Snakemake pipeline for Nextflow.
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## I have another issue, how to fix it?
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## I have another issue; how do I fix it?
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Don't hesitate to open an issue on [Sopa's Github repository](https://github.com/gustaveroussy/sopa/issues), and detail your issue with as much precision as possible, in order for the maintainers to be able to reproduce it.
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Don't hesitate to open an issue on [Sopa's Github repository](https://github.com/gustaveroussy/sopa/issues), and detail your issue with as much precision as possible for the maintainers to be able to reproduce it.
Now, follow our [snakemake tutorial](../tutorials/snakemake) to run your first pipeline.
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!!! Note
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You can also use a separate environment for `snakemake`. In this case, you don't need to install the `'snakemake'` extra when installing `sopa`. But you may still need to install other extras, for instance `'cellpose'` if you plan to run Cellpose.
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You can also use a separate environment for `snakemake`. In this case, you don't need to install the `'snakemake'` extra when installing `sopa`. But you may still need to install other extras, for instance,`'cellpose'` if you plan to run Cellpose.
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## Usage
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Sopa comes with three different flavors, each corresponding to a different use case:
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Sopa comes in three different flavours, each corresponding to a different use case:
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-`Snakemake pipeline`: choose a config, and run our pipeline on your spatial data in a couple of minutes. See our [snakemake tutorial](../tutorials/snakemake).
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-`Snakemake pipeline`: choose a config, and run our pipeline on your spatial data in a few minutes. See our [snakemake tutorial](../tutorials/snakemake).
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-`CLI`: use our [command-line-interface](../tutorials/cli_usage) to prototype quickly your own pipeline
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-`API`: use directly `sopa` as a python package for full flexibility and customization (see a tutorial [here](../tutorials/api_usage))
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-`API`: use directly `sopa` as a Python package for full flexibility and customization (see a tutorial [here](../tutorials/api_usage))
Built on top of [SpatialData](https://github.com/scverse/spatialdata), Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and low-memory consumption on large images (scales to `1TB+` images).
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Built on top of [SpatialData](https://github.com/scverse/spatialdata), Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and lowmemory consumption on large images (scales to `1TB+` images).
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The pipeline outputs contain: (i) Xenium Explorer files for interactive visualization, (ii) a HTML report for quick quality controls, and (iii) a SpatialData `.zarr` directory for further analyses.
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The pipeline outputs contain: (i) Xenium Explorer files for interactive visualization, (ii) an HTML report for quick quality controls, and (iii) a SpatialData `.zarr` directory for further analyses.
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## Overview
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## Why use `sopa`
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-`sopa` is designed to be memory-efficient, and it scales to large datasets with millions of cells
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- Depending on your need, you case use our Snakemake pipeline, our CLI, or our API
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- It's very easy to move on to another spatial-omics technology, since `sopa` is general to every image-based spatial-omics
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- You can open any data with the [Xenium Explorer](https://www.10xgenomics.com/support/software/xenium-explorer), which is a user-friendly software with many functionnalities
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- Spatial statistics are optimized, since geometric operations uses`shapely` internally
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- Depending on your need, you can use our Snakemake pipeline, our CLI, or our API
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- It's straightforward to move on to another spatial-omics technology since `sopa` is general to every image-based spatial-omics
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- You can open any data with the [Xenium Explorer](https://www.10xgenomics.com/support/software/xenium-explorer), which is a user-friendly software with many functions
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- Spatial statistics are optimized since geometric operations use`shapely` internally
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- You can customize `sopa` and add your own segmentation or annotation tool if desired
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-`sopa` integrates naturally with other community tools such as [Scanpy](https://scanpy.readthedocs.io/en/stable/index.html) or [Squidpy](https://squidpy.readthedocs.io/en/latest/index.html).
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