Welcome to RNA2seg’s documentation!¶
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.
Check out the Installation Guide section for further information about how to install the package.
released version and code¶
the package code is at https://github.com/fish-quant/rna2seg
- 12/03/25 RNA2seg 0.0.7 :
fix RNA embbeding bug
add pretrained model for brain data
Contents¶
Support¶
If you have any questions relative to the package, please open an issue on GitHub.
Citation¶
If you use this library, please be sure to cite:
@article {Defard2025.03.03.641259,
author = {Defard, Thomas and Blondel, Alice and Coleon, Anthony and Dias de Melo, Guilherme and Walter, Thomas and Mueller, Florian},
title = {RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics},
elocation-id = {2025.03.03.641259},
year = {2025},
doi = {10.1101/2025.03.03.641259},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2025/03/11/2025.03.03.641259},
eprint = {https://www.biorxiv.org/content/early/2025/03/11/2025.03.03.641259.full.pdf},
journal = {bioRxiv}
}