Mapping cell locations via multi-layer regionalization constraints
Introduction
Resolving spatial cell arrangement is crucial for understanding physiological and pathological processes. While scRNA-seq captures gene expression at single-cell resolution, it loses spatial context, and current spatial transcriptomics methods often compromise on throughput or resolution. Existing integration methods face challenges with accuracy and scalability due to noise from molecular diffusion, cell segmentation errors, and disproportionate cell-type representation. We present Polyomino, an algorithm framework employing multi-layered regional constraints to accurately assign cell locations, enhancing spatial accuracy and resilience to noise. Comparative analysis on benchmark datasets demonstrates Polyomino’s superior accuracy and scalability over existing methods. Applied to liver cancer tissue, Polyomino revealed spatial heterogeneity of cDC cells, a detail missed by deconvolution-based techniques, and achieved cell-cell interaction resolution beyond traditional mapping approaches. Additionally, Polyomino outperforms current techniques in computational efficiency and resource usage, particularly with large-scale stereo-seq data, underscoring its potential for broad application.
.. image:: ./overview.png :width: 1200 :align: center :alt: Overview of Polyomino
Installation
Polyomino can be installed either through GitHub or PyPI.
To install from GitHub:
.. code-block:: bash
git clone https://github.com/caiquanyou/Polyomino
cd Polyomino
python setup.py install # or pip install .
Alternatively, install via PyPI using:
.. code-block:: bash
pip install Polyomino
Usage
After installation, Polyomino can be used in Python as follows:
.. code-block:: python
import Polyomino as plmo
Contributing
Contributions to Polyomino are welcome. Please refer to the project's issues and pull requests for areas where you can help.
License
(Include license information here if available)
Support and Contact
For support or to contact the developers, please use the project's GitHub Issues page.
Metadata
Release files for Polyomino-sc 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| Polyomino-sc-1.0.1.tar.gz | 27.4 kB | Without provenance | Details |
Release files / Polyomino-sc-1.0.1.tar.gz
| Download URL | Polyomino-sc-1.0.1.tar.gz |
|---|---|
| Size | 27.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
fd0bcf72ebfb4554774a8545873226016bd06f3f25ec8464e85b62b451ea20c0
|
|
BLAKE2b-256 checksum How to use checksums |
d08bb9fb18749015b1f8b07d687db376d14955ac525111234f48d2eb2d1b5a51
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.8.8
|
Security for Polyomino-sc 1.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.