Automated Explainable AI for Omics (AutoXAI4Omics): an Explainable Auto-AI tool for omics and tabular data

AutoXAI4Omics is a command line automated explainable AI tool that easily enable researchers to perform phenotype prediction from omics data (e.g., gene expression; microbiome data; or any tabular data) and any tabular data (e.g., clinical) using a range of ML models.

Key features include:

• preprocessing specific for omics data (optional)
• feature selection (optional)
• HPO (hyper-parameter optimization) of a variety of ML models including neural networks
• selection of the best ML model(s)
• generation of explainability and interpretability results (using SHAP and Eli5)
• generation of predictive performance scores (cvs files) and a series of visualisations (e.g., plots)
• json configuration file for the specification of preprocessing, ML models, hyperparameter tuning, plots and results produced
• wrapping of Keras and TensorFlow models to make them compatible with sklearn models
• prediction on new data using the best model
• packaged as a Docker container

Requirements

• Docker
  • installation: https://docs.docker.com/get-docker/
• Git
  • installation: https://github.com/git-guides/install-git
• Python 3.9 (only required if the user is planning on contributing to the development of the tool)

How to install AutoXAI4Omics

1. Clone this repo however you choose (cli command: git clone --single-branch --branch main git@github.ibm.com:BiomedSciAI-Innersource/AutoXAI4Omics.git)

2. Make sure docker is running (cli command: docker version, if installed the version information will be given)

3. Within the AutoXAI4Omics folder:

   1. Run the following cli command to build the image: ./build.sh -r
   2. Manually create a new folder called experiments. IMPORTANT NOTE: if training is run by mistake without first creating the experiments directory, and the directory is created while training, the directory needs to be removed and then created again before running training (has to do with access permissions).
   3. Make the experiments folder accessaible by running the following the directory where the experiment directory exists:

   chmod 777 -R experiments 

Citation

For citation of this tool, please reference this article:

• James Strudwick, Laura-Jayne Gardiner, Kate Denning-James, Niina Haiminen, Ashley Evans, Jennifer Kelly, Matthew Madgwick, Filippo Utro, Ed Seabolt, Christopher Gibson, Bharat Bedi, Daniel Clayton, Ciaron Howell, Laxmi Parida, Anna Paola Carrieri. bioRxiv 2024.03.25.586460; doi: https://doi.org/10.1101/2024.03.25.586460

User manual

Everything is controlled through a config dictionary, examples of which can be found in the configs/exmaples folder. For an explanation of all parameters, please see the CONFIG MANUAL.

The tool is launched in the cli using autoxai4omics.sh which has multiple flags, examples will be given below:

• -m this specifies what mode you want to run AutoXAI4Omics in the options are:
  • feature - Run feature selection on a input data set
  • train - Tune and train various machine learning models, generate plots and results
  • test - To test and evaluate the tuned and trained machine learning models on a completely different holdout dataset
  • predict - Use trained models to predict on unseen data
  • plotting - If the models have been tuned and trained (and therefore saved), the plots and results can be generated in isolation
  • bash - Use to open up a bash shell into the tool
• -c this is the filename of the config json within the AutoXAI4Omics/configs folder that is going to be given to AutoXAI4Omics
• -r this sets the contain to run as root. Only possibly required if you are running in bash mode
• -d this detatches the cli running the container in the background
• -g this specifies if you want AutoXAI4Omics to use the gpus that are available on the machine (UNDER TESTING)

Data to be used by AutoXAI4Omics needs to be stored in the AutoXAI4Omics/data folder.

Examples

• Run AutoXAI4Omics in training mode with a config called my_fun_config.json within the configs folder:

  • ./autoxai4omics.sh -m train -c my_fun_config.json

• We have provided and example config and dataset that you can run to get going. The components are:

  • config: configs/examples/50k_barley_SHAP.json
  • data: data/geno_row_type_BRIDGE_50k_w.hetero.csv
  • metadata: data/row_type_BRIDGE_pheno_50k_metadata_w.hetero.csv
  • cli command to run: ./autoxai4omics.sh -m train -c examples/50k_barley_SHAP.json

• If you wish to run a bash shell within the AutoXAI4Omics image then you can do it using the following. In addition if you wish to be logged in as root add the -r flag:

  • ./autoxai4omics.sh -m bash -r

• UNDER DEVELOPMENT If you wish to utilise any gpus that are available on your machine during your AutoXAI4Omics run then you can add the -g flag:

  • ./autoxai4omics.sh -m train -c my_fun_config.json -g