Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations
tsai==0.3.1
sktime==0.14.1
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
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]
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]
For step importance & feature importance, make sure you are running tsai 0.3.1.
python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --step_importance True
python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --feature_importance True
python run.py --X_path [PATH_OF_X] --Y_path [PATH_OF_Y] --grad_cam True
For univariate GWAS we employed plink2.
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
--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
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
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