Skip to content
Adaptive Monte Carlo Multiple Testing
Jupyter Notebook Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
__pycache__
amt.egg-info
amt
experiments
README.md
setup.py

README.md

AMT

Software accompanying the paper "Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits"

Installation

  • Go to the /amt directory
  • Development installation pip install -e .
  • Python version: Python 3.6.3 :: Anaconda custom (64-bit)

Reproducing the experiments in the paper

Relative file paths are used.

Simulations

  • Progression (Figure 2-3): ./amt/experiments/simulation_progression.ipynb
  • Reliability (Table 1): ./amt/experiments/simulation_progression.ipynb Skip the Simulation section and directly run the Generate figures section to have the number in Table 1.
  • Scaling (Figure 4): ./amt/experiments/simulation_nMC.ipynb Skip the Simulation section and directly run the Generate figures section to have Figure 4.
  • Varying nominal FDR (Figure 5a): ./amt/experiments/simulation_alpha.ipynb Skip the Simulation section and directly run the Generate figures section to have Figure 5a.
  • Varying alternative proportion (Figure 5b): ./amt/experiments/simulation_pi1.ipynb Skip the Simulation section and directly run the Generate figures section to have Figure 5b.
  • Varying effect size (Figure 5c): ./amt/experiments/simulation_effect.ipynb Skip the Simulation section and directly run the Generate figures section to have Figure 5b.

GWAS data on Parkinson's disease

Unfortunately, the GWAS data is not included due to its large size. The data will be hosted online with the publication of the paper. Nonetheless, all results are recorded in corresponding notebooks.

  • Small GWAS (Table 2-3, Supp Table 1): ./amt/experiments/small_gwas_chr1.ipynb Changing chr1 to chrx (x=1,2,3,4) gives results on other chromosomes. Compute fMC p-values runs fMC and save the corresponding result, which also gives fMC running time. Result analysis prints fMC p-values for SNPs reported in the original paper. Corresponding AMT result runs AMT with the same MC samples. This is to check if AMT result is the same as fMC. This also gives the average number of MC samples AMT takes. Directly run AMT gives the running time of AMT, where the MC sampling is by actual permutation.
  • full GWAS: the experiment is done using python script ./amt/experiments/amt_gwas.py. This may take an hour to run and the results are stored in ./results/GWAS/result_amt_alpha_0.1_rep0, which gives running time and the average number of MC samples used. Analysis of the result is done using ./amt/experiments/parkinsons_analysis_full.ipynb, which prints out all information of the SNPs reported in the original paper.
You can’t perform that action at this time.