scPDAC

scPDAC maps and annotates single-cell RNA-seq data against pancreatic ductal adenocarcinoma (PDAC) reference atlases. It ships pretrained models for human and mouse and exposes two complementary workflows:

• 🧬 Atlas mapping — project a query dataset into a reference SCANVI latent space, either with scArches surgery (scpdac.tl.extend_atlas, tolerates new batches and returns an expanded atlas) or a fast embed-only label transfer (scpdac.tl.embed_and_predict) — all while retaining your original metadata.
• 🏷️ Hierarchical annotation — label cells with a 3-model hierarchical MLP classifier (scpdac.tl.predict_labels) that first splits Malignant vs Non-Malignant and then assigns fine-grained cell types with a dedicated sub-classifier for each branch.

Both paths align your genes to the model's panel automatically and write the predictions straight back into your AnnData.

Getting started

Please refer to the documentation. The atlas mapping and hierarchical classifier tutorials walk through both workflows end-to-end, the performance page reports held-out benchmarks and known limitations, and the API documentation lists every public function.

Installation

You need to have Python 3.11 or newer installed on your system. If you don't have Python installed, we recommend installing uv.

1. Install the latest release of scPDAC from PyPI:

pip install scPDAC

Release notes

See the changelog.

Contact

For questions and help requests, or to report a bug, please open an issue.

Citation

If you use scPDAC in your research, please cite:

Lucarelli D, Parikh S, Jiménez S, et al. Cross-species single-cell atlases chart progression, therapy-driven remodelling and immune evasion in pancreatic cancer. bioRxiv (2026). doi:10.64898/2026.03.19.712924

@article{Lucarelli2026,
  author    = {Lucarelli, Daniele and Parikh, Shrey and Jim{\'e}nez, Sara and others},
  title     = {Cross-species single-cell atlases chart progression, therapy-driven remodelling and immune evasion in pancreatic cancer},
  journal   = {bioRxiv},
  year      = {2026},
  doi       = {10.64898/2026.03.19.712924},
  publisher = {Cold Spring Harbor Laboratory},
  url       = {https://www.biorxiv.org/content/10.1101/2026.03.19.712924},
}