Metadata-Version: 2.2
Name: mmcci
Version: 1.0.1
Summary: A Python package for processing multi-platform, multi-sample CCI data
Home-page: https://github.com/GenomicsMachineLearning/MMCCI
Author: Genomics and Machine Learning lab
Author-email: l.hockey@uq.edu.au
Project-URL: Bug Tracker, https://github.com/GenomicsMachineLearning/MMCCI/issues
Project-URL: repository, https://github.com/GenomicsMachineLearning/MMCCI
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy<2.0.0,>=1.24.0
Requires-Dist: pandas>=2.1.4
Requires-Dist: networkx>=3.3.0
Requires-Dist: matplotlib<4.0.0,>=3.8.2
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: scipy>=1.9.1
Requires-Dist: tqdm>=4.66.1
Requires-Dist: gseapy>=1.1.1
Requires-Dist: scanpy>=1.9.0
Requires-Dist: seaborn>=0.11.2
Requires-Dist: anndata<=0.10.8

# MMCCI: Multi-platform, Multi-sample Cell-Cell Interaction Integrative Analysis of Single Cell and Spatial Data

<table align="center">
  <tr>
    <td>
      <b>Package</b>
    </td>
    <td>
      <a href="https://pypi.python.org/pypi/mmcci/">
      <img src="https://img.shields.io/pypi/v/mmcci.svg" alt="PyPI Version">
      </a>
      <a href="https://pepy.tech/project/mmcci">
      <img src="https://static.pepy.tech/personalized-badge/mmcci?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads"
        alt="PyPI downloads">
    </td>
  </tr>
  <tr>
    <td>
     <b>Paper</b>
    </td>
    <td>
      <a href="https://www.biorxiv.org/content/10.1101/2024.02.28.582639v3"><img src="https://zenodo.org/badge/DOI/10.1101/2023.05.14.540710.svg"
        alt="DOI"></a>
    </td>
  </tr>
  <tr>
    <td>
      <b>License</b>
    </td>
    <td>
      <a href="https://github.com/GenomicsMachineLearning/MMCCI/blob/main/LICENSE.txt"><img src="https://img.shields.io/badge/License-BSD-blue.svg"
        alt="LICENSE"></a>
    </td>
  </tr>
</table>
        
**MMCCI** is a fast and lightweight Python package for integrating and visualizing CCI networks within and between multiple platforms at the level of the individual LR pair. It works on **scRNA-seq** and **spatial transcriptomics** data samples that have been processed through the following CCI algorithms:
1. stLearn
2. CellChat
3. CellPhoneDB
4. NATMI
5. Squidpy

## Getting Started

### Installation

MMCCI can be installed with `pip`

```
pip install mmcci
```


### Documentation

Documentation and Tutorials are available here and we are commited to maintaining the software and addressing issues raised by users.

- There is a brain aging tutorial notebook [here](examples/brain_aging_example.ipynb)
- There is a melanoma tutorial notebook [here](examples/melanoma_example.ipynb)
- To understand how to load CCI results from different tools, look at this notebook [here](examples/loading_CCI_results.ipynb)

## CCI Integration

MMCCI allows users to integrate multiple CCI results together, both:
1. Samples from a single platform (eg. Visium)
2. Samples from multiple platforms (eg. Visium, Xenium and CosMX)

![Integration and Analysis Method](docs/images/analyses_pipeline.png)

## CCI Analysis

MMCCI provides multiple useful analyses that can be run on the integrated networks or from a single sample:
1. Network comparison between groups with permutation testing
2. CLustering of LR pairs with similar networks
3. Clustering of spots/cells with similar interaction scores
4. Sender-receiver LR querying
5. GSEA pathway analysis

## Citing MMCCI

If you have used MMCCI in your research, please consider citing us: 
```

```




BSD License

Copyright (c) 2024, Genomics and Machine Learning lab
All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice, this
  list of conditions and the following disclaimer in the documentation and/or
  other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from this
  software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
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BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
OF THE POSSIBILITY OF SUCH DAMAGE.
