cellpin 0.1.1

Cellpin: A Student–Teacher Variational Model for Spatial Transcriptomics Imputation and Encoding

cellpin

cellpin is a lightweight probabilistic model that reconstructs and denoises spatial transcriptomes from single-cell RNA-seq references. It enables transcriptome-wide imputation, robust atlas-to-spatial label-transfer, and improved biological interpretation of both targeted-panel and full-transcriptome spatial datasets. Read the documentation for detailed tutorials.

Quickstart

import cellpin
from torch.utils.data import DataLoader

sc_ds, st_ds = cellpin.pp.setup_data(sc_adata, st_adata)
model = cellpin.CellPin(sc_ds)
model.fit(sc_ds)

loader = DataLoader(st_ds, batch_size=256, shuffle=False)
adata_out = model.impute(loader, obs_adata=st_adata)

Installation

pip

pip install cellpin

uv

uv pip install cellpin

For SpatialData support add the spatial extras:

pip install "cellpin[spatial]"
# or
uv pip install "cellpin[spatial]"

Documentation

Full documentation, tutorials, and API reference: cellpin.readthedocs.io

Citation

If you use cellpin in your research, please cite:

Putze P*, Lucarelli D*, Wellappili D, Bahrami M, Luecken MD, Theis FJ, Saur D. Cellpin enables reference-based imputation and denoising of spatial transcriptomes. bioRxiv 2026.06.02.729566. doi: 10.64898/2026.06.02.729566

* Shared first authorship

@article{putze2026cellpin,
  title   = {Cellpin enables reference-based imputation and denoising of spatial transcriptomes},
  author  = {Putze, Philipp and Lucarelli, Daniele and Wellappili, Deelaka and Bahrami, Mojtaba and Luecken, Malte D. and Theis, Fabian J. and Saur, Dieter},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.02.729566},
  url     = {https://doi.org/10.64898/2026.06.02.729566}
}