nichepca 0.0.3

Package for PCA-based spatial domain identification in single-cell spatial transcriptomics data.

NichePCA: PCA-based spatial domain identification with state-of-the-art performance

NichePCA is a package for PCA-based spatial domain identification in single-cell spatial transcriptomics data. The corresponding manuscript was published in Bioinformatics.

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][].

There are several alternative options to install nichepca:

1. Install the latest release of nichepca from PyPI:

pip install nichepca

2. Install the latest development version:

pip install git+https://github.com/imsb-uke/nichepca.git@main

Getting started

Please refer to the documentation. In particular, the API documentation.

Given an AnnData object adata, you can run nichepca starting from raw counts as follows:

import scanpy as sc
import nichepca as npc

npc.wf.nichepca(adata, knn=25)
sc.pp.neighbors(adata, use_rep="X_npca")
sc.tl.leiden(adata, resolution=0.5)

Multi-sample support

If you have multiple samples in adata.obs["sample"], you can provide the key sample to npc.wf.nichepca this uses harmony by default:

npc.wf.nichepca(adata, knn=25, sample_key="sample")

If you have cell type labels in adata.obs["cell_type"], you can directly provide them to nichepca as follows (we found this sometimes works better for multi-sample domain identification). However, in this case we need to run npc.cl.leiden_unique to handle potential duplicate embeddings:

npc.wf.nichepca(adata, knn=25, obs_key="cell_type", sample_key="sample")
npc.cl.leiden_unique(adata, use_rep="X_npca", resolution=0.5, n_neighbors=15)

Customization

The nichepca function also allows to customize the original ("norm", "log1p", "agg", "pca") pipeline, e.g., without median normalization:

npc.wf.nichepca(adata, knn=25, pipeline=["log1p", "agg", "pca"])

or with "pca" before "agg":

npc.wf.nichepca(adata, knn=25, pipeline=["norm", "log1p", "pca", "agg"])

or without "pca" at all:

npc.wf.nichepca(adata, knn=25, pipeline=["norm", "log1p", "agg"])

Hyperparameter choice

We found that higher number of neighbors e.g., knn=25 lead to better results in brain tissue, while knn=10 works well for kidney data. We recommend to qualitatively optimize these parameters on a small subset of your data. The number of PCs (n_comps=30 by default) seems to have negligible effect on the results.

Contributing

If you want to contribute you can follow this guide. In short fork the repository, setup a dev environment using this command:

git clone https://github.com/{your-username}/nichepca.git
cd nichepca
uv sync --all-extras

And then make your changes, run the tests and submit a pull request.

Release notes

See the changelog.

Contact

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

Citation

If you use NichePCA in your research, please cite:

@article{schaub2025pca,
  title={PCA-based spatial domain identification with state-of-the-art performance},
  author={Schaub, Darius P and Yousefi, Behnam and Kaiser, Nico and Khatri, Robin and Puelles, Victor G and Krebs, Christian F and Panzer, Ulf and Bonn, Stefan},
  journal={Bioinformatics},
  volume={41},
  number={1},
  pages={btaf005},
  year={2025},
  publisher={Oxford University Press}
}