TargetedEstimation

This package provides two command line interfaces used mainly in the context of TarGene:

1. scripts/tmle.jl: To run Targeted Maximum Likelihood Estimation
2. scripts/sieve_variance.jl: To run sieve variance correction to account for potential non iid data.

Usage

The best way to use the command lines is to use the associated docker image. Command line arguments can be displayed by:

tmle.jl

To display command line arguments:

julia --project=/TargetedEstimation.jl --startup-file=no scripts/tmle.jl --help

sieve_variance.jl

This requires an HDF5 file output by tmle.jl and the Genetic Relationship Matrix output by the GCTA software.

To display command line arguments:

julia --project=/TargetedEstimation.jl --startup-file=no scripts/sieve_variance.jl --help

Experiments

The experiments contains various experiments related to genetic association studies: GWAS' and PheWAS'.

GWAS Runtime

The goal of this experiment is to estimate the running time of TMLE in a GWAS setting. Because the propensity score estimation runtime varies for various SNPs, this is done by running TMLE over 100 SNPs. We estimate the runtime for both a continuous and a binary target and for 4 nuisance parameters specifications: GLM, GLMNet, CrossValidatedXGBoost, Super Learning(GLMNet+CrossValidatedXGBoost). Cross validations selections are performed over 3-folds.

• Associated data: Restricted access. On the University of Edinburgh datastore, /exports/igmm/datastore/ponting-lab/olivier/misc_datasets/gwas_sample_data.csv

• Associated script: experiments/gwas_runtime.jl.

• Julia script usage: julia --project --startup-file=no experiments/gwas_runtime.jl --help

• Bash script (to submit jobs on the Eddie cluster):

  • qsub experiments/gwas_unit_binary.sh
  • qsub experiments/gwas_unit_continuous.sh

PheWAS Runtime

The goal of this experiment is to estimate the running time of TMLE in a PheWAS setting. Since the propensity score is estimated only once, it is not driving runtime. The PheWAS is perfomed over more than 760 traits and for 4 nuisance parameters specifications: GLM, GLMNet, CrossValidatedXGBoost, Super Learning(GLMNet+CrossValidatedXGBoost). Cross validations selections are performed over 3-folds.

• Associated data: Restricted access. On the University of Edinburgh datastore, /exports/igmm/datastore/ponting-lab/olivier/misc_datasets/sample_ukb_data.csv

• Associated script: experiments/phewas_runtime.jl.

• Julia script usage: julia --project --startup-file=no experiments/phewas_runtime.jl --help

• Bash scripts (to submit jobs on the Eddie cluster):

  • qsub experiments/phewas_glm.sh
  • qsub experiments/phewas_glmnet.sh
  • qsub experiments/phewas_xgboost.sh
  • qsub experiments/phewas_sl.sh