Skip to main content

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 CellChIP, 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 CellChIP’s superior accuracy and scalability over existing methods. Applied to liver cancer tissue, CellChIP revealed spatial heterogeneity of cDC cells, a detail missed by deconvolution-based techniques, and achieved cell-cell interaction resolution beyond traditional mapping approaches. Additionally, CellChIP 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 CellChip

Installation

CellChip can be installed either through GitHub or PyPI.

To install from GitHub:

.. code-block:: bash

git clone https://github.com/caiquanyou/CellChip
cd CellChip
python setup.py install # or pip install .

Alternatively, install via PyPI using:

.. code-block:: bash

pip install CellChip

Usage

After installation, CellChip can be used in Python as follows:

.. code-block:: python

import CellChip as cc

Contributing

Contributions to CellChip 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 CellChip 1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for CellChip 1.2
File Size Uploaded Provenance
CellChip-1.2.tar.gz 27.3 kB Without provenance Details

Release files / CellChip-1.2.tar.gz

Download URL CellChip-1.2.tar.gz
Size 27.3 kB
Tags Source
SHA-256 checksum
How to use checksums
6f1448a60a34e178f1ea86d4ad505a15f47e3da8f009881a504d0d482ce0a892
BLAKE2b-256 checksum
How to use checksums
bc34b360767dca37eef38751adde0dc4369fe77cd88e255eb74dfd105503df04
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.8.8

Security for CellChip 1.2

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.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page