omiembed's Introduction

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 ([email protected])

Data Science Institute, Imperial College London

Introduction

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

Multi-omics integration
Dimensionality reduction
Omics embedding learning
Tumour type classification
Phenotypic feature reconstruction
Survival prediction
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}
}