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OmiEmbed

Please also have a look at our brand new omics-to-omics DL freamwork 👀: OmiTrans

OmiEmbed: A Unified Multi-task Deep Learning Framework for Multi-omics Data

Xiaoyu Zhang (x.zhang18@imperial.ac.uk)

Data Science Institute, Imperial College London

Introduction

OmiEmbed is a unified framework for deep learning-based omics data analysis, which supports:

1. Multi-omics integration
2. Dimensionality reduction
3. Omics embedding learning
4. Tumour type classification
5. Phenotypic feature reconstruction
6. Survival prediction
7. Multi-task learning for aforementioned tasks

Paper Link: https://doi.org/10.3390/cancers13123047

Getting Started

Prerequisites

• CPU or NVIDIA GPU + CUDA CuDNN
• Python 3.6+
• Python Package Manager
  • Anaconda 3 (recommended)
  • or pip 21.0+
• Python Packages
  • PyTorch 1.2+
  • TensorBoard 1.10+
  • Tables 3.6+
  • scikit-survival 0.6+
  • prefetch-generator 1.0+
• Git 2.7+

Installation

• Clone the repo

git clone https://github.com/zhangxiaoyu11/OmiEmbed.git
cd OmiEmbed

• Install the dependencies
  • For conda users

  conda env create -f environment.yml
  conda activate omiembed
  

  • For pip users

  pip install -r requirements.txt
  

Try it out

• Train and test using the built-in sample dataset with the default settings

python train_test.py

• Check the output files

cd checkpoints/test/

• Visualise the metrics and losses

tensorboard --logdir=tb_log --bind_all

Citation

If you use this code in your research, please cite our paper.

@Article{OmiEmbed2021,
    AUTHOR = {Zhang, Xiaoyu and Xing, Yuting and Sun, Kai and Guo, Yike},
    TITLE = {OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data},
    JOURNAL = {Cancers},
    VOLUME = {13},
    YEAR = {2021},
    NUMBER = {12},
    ARTICLE-NUMBER = {3047},
    ISSN = {2072-6694},
    DOI = {10.3390/cancers13123047}
}

OmiTrans

Please also have a look at our brand new omics-to-omics DL freamwork 👀: OmiTrans

License

This source code is licensed under the MIT license.