Metadata-Version: 2.1
Name: multimodal_cci
Version: 1.0.1
Summary: A Python package for processing multi-modal CCI data
Home-page: https://github.com/BiomedicalMachineLearning/MultimodalCCI
Author: Genomics and Machine Learning lab
Author-email: l.hockey@uq.edu.au
Project-URL: Bug Tracker, https://github.com/BiomedicalMachineLearning/MultimodalCCI/issues
Project-URL: repository, https://github.com/BiomedicalMachineLearning/MultimodalCCI
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>=1.24.0
Requires-Dist: pandas>=2.1.4
Requires-Dist: networkx>=3.2.1
Requires-Dist: matplotlib>=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


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# MMCCI: Multimodal Cell-Cell Interaction Integration, Analysis, and Visualisation

**MMCCI** is a fast and lightweight Python package for integrating and visualizing CCI networks within and between multiple modalities 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

Coming soon

### Documentation

Documentation and Tutorials are available at our **Read the Docs** page (coming soon).

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

## CCI Integration

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

![Integration Method](docs/images/integration_method.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

![Downstream Analyses](docs/images/analyses.png)

### Pipeline Diagram

![MMCCI Pipeline](docs/images/pipeline.png)

## Citing MMCCI

If you have used MMCCI in your research, please consider citing us: (coming soon).




BSD License

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

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