Confounder Control in Biomedicine Necessitates Conceptual Considerations Beyond Statistical Evaluations

General information

This repository contains all the code needed to run the analyses for the paper "Confounder Control in Biomedicine Necessitates Conceptual Considerations Beyond Statistical Evaluations" (https://www.medrxiv.org/content/10.1101/2024.02.02.24302198v2.full-text).

Data

This research has been conducted using data from UK Biobank, a major biomedical database (www.ukbiobank.ac.uk). Behavioural variables are derived from the UKB using the ukbb_parser in a modified version to be able to parse .tsv files (https://github.com/kaurao/ukbb_parser/tree/filetype_unknown). Most neuroimaging data are consumed using datalad (https://www.datalad.org). The data can not be made available here as it has constrained access.

Compute information

Most computations were run on a high throughput compute cluster with HTCondor scheduler. All scripts theoretically can be run on a single (modern) machine, but especially the feature extraction and prediction parts will run very long on a single machine.

Repository structure

The repository follows the following folder structure:

1. /data: contains FC features, other features derived using DataLad
2. /lib: reusable code (functions, modules)
3. /src: scripts enumerated in consecutive order according to analysis
4. /results: output directory for scripts, follows the same ordering as /src

Creating the environment:

1. Create a conda or mamba environment using the provided requirements.yaml:

mamba env create -f requirements.yaml 

(The ukbb_parser must be manually installed from the github branch listed above.)

2. After having set up the enviornment, to make the repository internal library structure available got o ./lib (directory where the setup.py) is located and run:

python setup.py develop

Code explanations (/src)

In general, follow the respective numbering of subfolders and scripts within subfolders. If a script was executed on the cluster a .submit witht the same name as the to be executed python file exists. All code should be run in the root directory of the repository. Initial directories in .submit files will need to be adapted to indivual setups.

1. feature extraction (./src/1_feature_extraction/...)
   • GMV
     1. generate submit and dag files e.g. python ./src/1_feature_extraction/1_generate_submit_dag_gmd_Schaefer.py
     2. submit dag: condor_submit_dag -import_env ./src/1_feature_extraction/1_gmd_schaefer.dag (and respectively for other atlases)
     3. merge single subject databases: e.g. condor_submit ./src/1_feature_extraction/4_merge_gmd_SUIT_databases.submit (and respectively for other atlases)
   • FC: data from costum code from different project -> put FC.csv features in ./data/functional.
   • Convert .sqlite feature databases to .jay format for quicker IO: python ./src/1_feature_extraction/7_convert_features2jay.py
2. phenotyoe extraction (./src/2_phenotype_extraction/...)
   • get HGS target phenotypes and extract imaging subjects: either run 1_get_motor_phenotypes.py or submit to cluster with .submit
   • clean the HGS target phenotypes: 2_clean_motor_phenotypes_IMG.py
   • get all UKB phenotypes for stats CC (and extract imaging subjects): run 3_get_possibleUKB_phenotypes.py by submitting 3_get_possibleUKB_phenotypes.submit
   • clean the UKB phenotypes: run 4_clean_possibleUKB_phenotypes.py
   • Get TIV: 5_get_TIV.py
3. statistical continuum (stats+visualization): (./src/3_statistical_continuum/...)
   • Calculate the correlation of GMV and HGS with potential confounders: ./3_statistical_continuum/1_ConfoundImportance_allUKB.py
   • Visualize the stats CC: 2_Visualize_confoundImportance_allUKB.py
4. predictions: (./src/4_predictions/...)
   • Create the pipeline options: ./4_prediction/1_create_pipeline_options.py
   • Script that actually performs the predictions and makes a OOS prediction plot for each run in 5 different colours: 2_predict.py
   • 2_predict.submit -> submit file to run all jobs created in ./4_prediction/1_create_pipeline_options.py with the prediction script 2_predict.py
   • 3_merge_prediction_summaries: Read in all single prediction summaries DFs and merge into one summary DF