RNA2seg is a deep learning-based segmentation model designed to improve cell segmentation in Imaging-based Spatial Transcriptomics (IST). Traditional IST methods rely on nuclear and membrane staining to define cell boundaries, but segmentation can be challenging due to the variable quality of membrane markers.
RNA2seg addresses this issue by integrating an arbitrary number of staining channels along with RNA spatial distributions to enhance segmentation accuracy, particularly in regions with low-quality membrane staining. It is built on SpatialData, enabling seamless processing and analysis of spatial transcriptomics data.
- Multi-channel input: Leverages nuclear, membrane, and RNA spatial data.
- Robust segmentation: Outperforms state-of-the-art methods across multiple datasets.
- Zero-shot learning: Generalizes to unseen datasets without requiring manual annotations.
- Built on SpatialData: Ensures compatibility with modern spatial transcriptomics pipelines.
RNA2seg is designed for researchers working with spatial transcriptomics data who need accurate and adaptable segmentation across different experimental conditions.
Check RNA2seg's documentation to get started. It contains installation explanations and tutorials.
It is recommended to create a virtual environment before installing RNA2seg to isolate dependencies:
$ python -m venv rna2seg-env
$ source rna2seg-env/bin/activate
Then, install RNA2seg and its dependencies:
(rna2seg-env) $ pip install rna2seg
(rna2seg-env) $ pip install -r requirements.txt
RNA2seg is now installed and ready to use. 🚀
If you use this library, please be sure to cite:
@article{}
All datasets, required to reproduce the results of publication "RN2Aseg: a generalist model for cell segmentation1 in image-based spatial transcriptomics2" are available at ...