ZuqiLi/Multi-view-Deconfounding-VAE

Project overview

• Aim: develop a Multi-view deconfounding VAE (multi-view data integration + confounder correction)
• Data:
  • Rotterdam study
    • 500 individuals
    • Cardiovascular diseases
    • Methylation
    • 3D facial images
  • Toy data (TCGA)
    • 2547 patients
    • 6 cancers
    • 2000 most variable mRNAs
    • 2000 most variable DNAm
• Conducted by Sonja Katz and Zuqi Li
• Supervised by Prof. Kristel Van Steen, Dr. Gennady Roshchupkin and Prof. Vitor Martins Dos Santos
• Google folder: https://drive.google.com/drive/folders/1GwZbMpVWW4xqdxmw_JRq9-DAR0WlnkE4

Installation

## cd Multi-view-Deconfounding-VAE
conda env create -f environment.yml
source activate env_multiviewVAE

Overview models:

• all models are in folder: models

• optimal architecture from modelOptimisation experiments: latentSize = 50; hiddenlayers = 200

• XVAE:

  • XVAE - Simidjievski, Nikola, et al. "Variational autoencoders for cancer data integration: design principles and computational practice." Frontiers in genetics 10 (2019): 1205.

• cXVAE:

  1. input+embed:
  2. input:
  3. embed:
  4. fused+embed:

• Adversarial Training

  1. XVAE with one adversarial network and multiclass predicition: adversarial_XVAE_multiclass

     • XVAE_adversarial_multiclass: inspired by Dincer et al.; training over all batches
     • XVAE_adversarial_1batch_multiclass: original by Dincer et al.

     
     

     • XVAE_scGAN_multiclass: inspired by Bahrami et al.

     
     

  2. XVAE with multiple adversarial network (one for each confounder): adversarial_XVAE_multipleAdvNet

     • XAE with multiple adversarial network (outdated?)

Workplan

• 1. Select basic model
  • Simidjievski, Nikola, et al. "Variational autoencoders for cancer data integration: design principles and computational practice." Frontiers in genetics 10 (2019): 1205.
  • https://github.com/CancerAI-CL/IntegrativeVAEs
  • The X-shaped Variational Autoencoder (X-VAE) Architecture is overall recommended in this comparative study
• 2. Reform the basic model
  • Implement in Pytorch Lightning
  • Rearrange code
  • Provide two latent loss functions (KL divergence and Maximum Mean Discrepancy)
  • Implement testing metrics
• 3. Create a clustering model
  • Strategy 1: Run K-means (or other clustering methods) on the latent space
  • Strategy 2: Add a term in the loss function to iteratively optimize the clustering quality
• 4. Correct for confounders

  • Strategy 1: Take confounders into account during decoding and the loss function is conditioned on the confounders; Adapted from: Lawry Aguila, Ana, et al. "Conditional VAEs for Confound Removal and Normative Modelling of Neurodegenerative Diseases." Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022.
    

    • Build cVAE - concat covariates to input dim & latent dim

  • Strategy 2: Add a term in loss function to minimize the association/similarity between the latent embedding and confounders
    

    • XVAE with adversarial training; Inspiration from this paper
      • simple version; only pre-training
      • ping pong training

  • Strategy 3: Simply remove confounded latent features