🧬 Comical: Contrastive multi-omics association learning

Due to the sophisticated nature of complex diseases, finding interpretable associations between multi-omics data can be challenging using standard approaches.

We propose a contrastive learning approach leveraging multi-omics data to generate many-to-many associations between any two types of multi-omics information. We generate learnable embeddings from tokenizations of each modality and utilize attention-based encoders to learn the connections between them.

Our modal-agnostic approach uniquely identifies many-to-many associations via self-supervised learning schemes and cross-modal attention encoders. Our method also provides a pre-trained model for many-to-many multi-omic association discovery.

Built With

Getting Started

Prerequisites

1. Create the environment from the comical_env.yml file:

conda env create -f comical_env.yml

• Note: if you receive the error bash: conda: command not found..., you need to install Anaconda to your development environment (see "Additional resources" below)

2. Activate the new environment:

conda activate comical-env

3. Verify that the new environment was installed correctly:

conda env list

• Additional resources:
  • Connect to computing cluster
  • Set up / install Anaconda on remote linux server
  • Set up remote development environment using VSCode

Running Comical

1. Request resources from computing cluster:

jbsub -cores 2+1 -q x86_1h -mem 800g -interactive bash

2. Activate the new environment:

conda activate comical-env

3. Move to directory with source code and data:

cd /dccstor/ukb-pgx/comical/comical

4. Run Comical:

nohup python wrapper.py --fname_out_root new_run_check_code --epochs 4 --top_n_perc 0.5 &

Help

python wrapper.py --help

Authors

Contributors and contact info:

• Diego Machado Reyes
• Myson Burch (myson dot burch at ibm dot com)
• Aritra Bose (a dot bose at ibm dot com)
• Laxmi Parida (parida at us dot ibm dot com)

Version History

• 0.1
  • Initial Release