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Feature subsetting framework for multi-omics data analysis

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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
  • 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

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Feature subsetting framework for multi-omics data analysis

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