SubOmiEmbed

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Paper: https://arxiv.org/abs/2202.01672

This codebase is built upon https://github.com/zhangxiaoyu11/OmiEmbed

Introduction

SubOmiEmbed is an extension of OmiEmbed that supports the SSL technique of feature subsetting for the following tasks.

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

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/hashimsayed0/OmiEmbed
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