This repository uses the simChef
simulation framework to reproduce a portion of the simulation results presented
in "A flexible approach for predictive biomarker
discovery" by Boileau et al. The method
presented in this preprint is implemented in the publicly available
uniCATE
R
package and the
original simulation results are available in the repository
pub_uniCATE
.
The simulation documentation and results generated by simChef
are found
here.
The list below summarizes this repository's contents:
R/
: The directory containing all of theR
code. The data-generating processes are indgps-functions/
, the methods inmethod-functions/
,simChef
evaluator functions inevaluator-functions/
, and the visualization functionality invisualizer-functons/
. The mainsimChef
file ismeal.R
; the simulation study code is found here.results/
: The results produced by runningR/meal.R
are saved here.slurm/
: This directory contains a bash script for runningR/meal.R
using SLURM schedulerlogs/
: The log file generated by the SLURM scheduler is saved in this directory.renv/
: This directory is automatically generated by therenv
R
package. Do not modify its contents manually.
You can reproduce the partial simulation study locally by running the contents
of the R/meal.R
file. Be sure to install the
renv
package first to ensure
that all required libraries are installed. R/meal.R
can also be run on an
high-performance computing environment. Checkout the
slurm/savio-run-experiment.sh
example SLURM script. You might also want to
modify the future
plan based on
the computational resources at your disposal.
If you use the simChef
package for your own simulation study, please cite it
with the following BibTeX entry:
@manual{simChef,
title = {simChef: Intensive Computational Experiments Made Easy},
author = {James Duncan and Tiffany Tang},
year = {2022},
note = {R package version 0.0.2},
url = {https://yu-group.github.io/simChef},
}
uniCATE
can be cited using the following entry:
@misc{boileau-2022,
doi = {10.48550/ARXIV.2205.01285},
url = {https://arxiv.org/abs/2205.01285},
author = {Boileau, Philippe and Qi, Nina Ting and van der Laan, Mark J. and Dudoit, Sandrine and Leng, Ning},
keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {A Flexible Approach for Predictive Biomarker Discovery},
publisher = {arXiv},
year = {2022}
}
© 2022 Philippe Boileau
The contents of this repository are distributed under the MIT license. See file
LICENSE
for details.