https://github.com/bowang-lab/integrao
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Science Score: 46.0%
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○CITATION.cff file
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✓Academic publication links
Links to: arxiv.org, nature.com -
✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (14.5%) to scientific vocabulary
Repository
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Basic Info
- Host: GitHub
- Owner: bowang-lab
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://www.nature.com/articles/s42256-024-00942-3
- Size: 15.4 MB
Statistics
- Stars: 56
- Watchers: 4
- Forks: 11
- Open Issues: 1
- Releases: 2
Metadata Files
README.md
IntegrAO: Integrate Any Omics
This is the official codebase for Integrate Any Omics: Towards genome-wide data integration for patient stratification.
Updates:
[2025.03.02] 🔥🔥🔥 We added the functionalities of extracting feature importance for the unsupervised and supervised IntegrAO models! Feel free to check it out here: Unsupervised integration feature importance and Supervised integration feature importance. Welcome for suggestions!
[2025.01.23] 🥳 IntegrAO is published on Nature Machine Intelligence!
[2024.01.15] 🥳 IntegrAO Preprint available!
🔨 Hardware requirements
IntegrAO package requires only a standard computer with enough RAM to support the in-memory operations.
🔨 Installation
IntegrAO works with Python >= 3.7. Please make sure you have the correct version of Python pre-installation.
- Create a virtual environment:
conda create -n integrAO python=3.10 -yandconda activate integrAO - Install Pytorch 2.1.0
- IntegrAO is available on PyPI. To install IntegrAO, run the following command:
pip install integrao
For developing, clone this repo with following commands:
bash
$ git clone this-repo-url
$ cd IntegrAO
$ pip install -r requirement.txt
🧬 Introduction
High-throughput omics profiling advancements have greatly enhanced cancer patient stratification. However, incomplete data in multi-omics integration presents a significant challenge, as traditional methods like sample exclusion or imputation often compromise biological diversity and dependencies. Furthermore, the critical task of accurately classifying new patients with partial omics data into existing subtypes is commonly overlooked. We introduce IntegrAO, an unsupervised framework integrating incomplete multi-omics and classifying new biological samples. IntegrAO first combines partially overlapping patient graphs from diverse omics sources and utilizes graph neural networks to produce unified patient embeddings.
An overview of IntegrAO can be seen below.
📖 Tutorial
We offer the following tutorials for demonstration:
- NEW: Unsupervised integration feature importance
- NEW: Supervised integration feature importance
- Integrate simulated butterfly datasets
- Integrate simulated cancer omics datasets
- Classify new samples with incomplete omics datasets
Citing IntegrAO
bash
@article{ma2025moving,
title={Moving towards genome-wide data integration for patient stratification with Integrate Any Omics},
author={Ma, Shihao and Zeng, Andy GX and Haibe-Kains, Benjamin and Goldenberg, Anna and Dick, John E and Wang, Bo},
journal={Nature Machine Intelligence},
volume={7},
number={1},
pages={29--42},
year={2025},
publisher={Nature Publishing Group}
}
}
GitHub Events
Total
- Create event: 4
- Release event: 1
- Issues event: 13
- Watch event: 47
- Delete event: 1
- Issue comment event: 19
- Member event: 1
- Push event: 25
- Pull request event: 14
- Fork event: 11
Last Year
- Create event: 4
- Release event: 1
- Issues event: 13
- Watch event: 47
- Delete event: 1
- Issue comment event: 19
- Member event: 1
- Push event: 25
- Pull request event: 14
- Fork event: 11
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 7
- Total pull requests: 7
- Average time to close issues: 12 days
- Average time to close pull requests: less than a minute
- Total issue authors: 5
- Total pull request authors: 2
- Average comments per issue: 3.29
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 7
- Pull requests: 7
- Average time to close issues: 12 days
- Average time to close pull requests: less than a minute
- Issue authors: 5
- Pull request authors: 2
- Average comments per issue: 3.29
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- marioernestovaldes (3)
- kjh-sdfmu (1)
- Dadatata-JZ (1)
- LudensZhang (1)
- liux0614 (1)
Pull Request Authors
- rexxxx1234 (7)
- npunw (5)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- python ^3.9
- matplotlib *
- numpy *
- pandas *
- snfpy *
- torch *
- torchvision *