🍳 Welcome to segger!

segger is a cutting-edge tool for cell segmentation in single-molecule spatial omics datasets. By leveraging graph neural networks (GNNs) and heterogeneous graphs, segger offers unmatched accuracy and scalability.

How segger Works

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Quick Links

• 💾 Installation Guide
  Get started with installing segger on your machine.

• 📖 User Guide
  Learn how to use segger for cell segmentation tasks.

• 💻 Command-Line Interface (CLI)
  Explore the CLI options for working with segger.

• 📚 API Reference
  Dive into the detailed API documentation for advanced usage.

• 📝 Sample Workflow
  Check out our tutorial showcasing a sample workflow with segger.

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Why segger?

• Highly parallelizable – Optimized for multi-GPU environments
• Fast and efficient – Trains in a fraction of the time compared to alternatives
• Transfer learning – Easily adaptable to new datasets and technologies

Challenges in Segmentation

Spatial omics segmentation faces issues like:

• Over/Under-segmentation
• Transcript contamination
• Scalability limitations

segger tackles these with a graph-based approach, achieving superior segmentation accuracy.

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Installation Options

Important: PyTorch Geometric Dependencies

Segger highly depends on PyTorch Geometric. One must install its dependencies (such as torch-sparse and torch-scatter) based on their system’s specifications, especially CUDA and PyTorch versions.

Please follow the official PyTorch Geometric Installation Guide to install the correct versions of torch-sparse, torch-scatter, and other relevant libraries.

Here’s how to install them manually, e.g., for torch 2.0.0:

For CUDA 11.x:

pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+cu117.html

For CUDA 12.x:

pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu120.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+cu120.html

Afterwards choose the installation method that best suits your needs.

Micromamba Installation

To set up Segger with micromamba and install the required dependencies, use the following commands:

micromamba create -n segger-rapids --channel-priority 1 \
    -c rapidsai -c conda-forge -c nvidia -c pytorch -c pyg \
    rapids=24.08 python=3.* 'cuda-version>=11.4,<=11.8' jupyterlab \
    'pytorch=*=*cuda*' 'pyg=*=*cu118' pyg-lib pytorch-sparse
micromamba install -n segger-rapids --channel-priority 1 --file mamba_environment.yml
micromamba run -n segger-rapids pip install --no-deps ./

GitHub Installation

For a straightforward local installation from GitHub, clone the repository and install the package using pip:

git clone https://github.com/EliHei2/segger_dev.git
cd segger_dev
pip install -e "."

Pip Installation (RAPIDS and CUDA 11)

For installations requiring RAPIDS and CUDA 11 support, run:

pip install -e ".[rapids11]"

Pip Installation (RAPIDS and CUDA 12)

For installations requiring RAPIDS and CUDA 12 support, run:

pip install -e ".[rapids12]"

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Powered by

• PyTorch Lightning & PyTorch Geometric: Enables fast, efficient graph neural network (GNN) implementation for heterogeneous graphs.
• Dask: Scalable parallel processing and distributed task scheduling, ideal for handling large transcriptomic datasets.
• Shapely & Geopandas: Utilized for spatial operations such as polygon creation, scaling, and spatial relationship computations.
• RAPIDS: Provides GPU-accelerated computation for tasks like k-nearest neighbors (KNN) graph construction.
• AnnData & Scanpy: Efficient processing for single-cell datasets.
• SciPy: Facilitates spatial graph construction, including distance metrics and convex hull calculations for transcript clustering.

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Contributions

segger is open-source and welcomes contributions. Join us in advancing spatial omics segmentation!

• Source Code
  GitHub

• Bug Tracker
  Report Issues

• Full Documentation
  API Reference