segger

GNN-based cell segmentation of spatial transcriptomics data.

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods.

—Heidari, Moorman, et al. (2025)

Preprint | GitHub

Contributing

See CONTRIBUTING.md.

Citation

If you use segger in your research, please cite:

Heidari, E., Moorman, A., et al. Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data. bioRxiv (2025). https://doi.org/10.1101/2025.03.14.643160

@article{heidari2025segger,
  title={Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data},
  author={Heidari, Elyas and Moorman, Andrew and others},
  journal={bioRxiv},
  year={2025},
  doi={10.1101/2025.03.14.643160}
}