Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Requirements

tsai==0.3.1
sktime==0.14.1

Installation

Create a conda environment with python 3.7:

conda create -n ABCD python=3.7

Activate the environment:

conda activate ABCD

Install ABCD and requirements:

pip install -e . --user

Example Running

cd into the ABCD folder:

cd ABCD

Run model with preprocessing:

python run.py --preprocess True --add_genomics False \
--raw_data_path [PATH_OF_RAW_LABEL] --group [GROUP_NAME] --label_path [PATH_OF_LABEL] \
--cov_path [PATH_OF_COVARIATES] --out_path [OUTPUT_PATH] --save_path [SAVE_PATH]

Run model with preprocessing and genomics data:

python run.py --preprocess True --add_genomics True --raw_data_path [PATH_OF_RAW_LABEL] \
--group [GROUP_NAME] --label_path [PATH_OF_LABEL] \
--cov_path [PATH_OF_COVARIATES] --out_path [OUTPUT_PATH] --save_path [SAVE_PATH]

Load preprocessed files and run model:

python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y]

Try Different Models

Supported models include 'RNN', 'TST', 'InceptionTimePlus', 'XceptionTimePlus', 'MiniRocket'. Run different models:

Load preprocessed files and run model:

python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --model [MODEL_NAME]

Model Interpretability

For step importance & feature importance, make sure you are running tsai 0.3.1.

Step Importance

python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --step_importance True

Feature Importance

python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --feature_importance True

GradCAM

python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --grad_cam True

GWAS

Univariate GWAS

For univariate GWAS we employed plink2.

Binary trait

plink2 \
  --threads 6 \
  --out out/gwas_sumstats \
  --keep indivs2keep.txt \
  --pfile imputed/pgen.files/genotype \
  --pheno phenotypes.binary.tsv \
  --chr 1-22,X \
  --covar covariates.tsv \
  --covar-variance-standardize \
  --glm firth-fallback hide-covar omit-ref no-x-sex

Continuous trait

  --threads 6 \
  --out out/gwas_sumstats \
  --keep indivs2keep.txt \
  --pfile imputed/pgen.files/genotype \
  --pheno phenotypes.quantitative.tsv \
  --pheno-name cbcl_externalizing,cbcl_internalizing,liability_Xception_without_cbcl,liability_Xception_with_CBCL,liability_Xception_without_cbcl_v2,liability_Xception_with_CBCL_v2,XGB_without_cbcl,XGB_with_cbcl,XGB_without_cbcl_v2,XGB_with_cbcl_v2 \
  --chr 1-22,X \
  --covar covariates.tsv \
  --covar-variance-standardize \
  --glm hide-covar omit-ref no-x-sex

Multivariate GWAS

For multivariate GWAS we employed mvgwas-nf.

nextflow run mvgwas.nf --l 1000 --geno all.chr.vcf.gz --pheno phenotypes.tsv --cov covariates.tsv --out mvgwas.tsv -resume -with-singularity -with-trace -bg -with-mpi