Code for reproducing analysis for "Measuring the contribution of genomic..."
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.BatchJobs.R
README.md
functions.R
genesigprecision_data.Rda
genesigprecision_run.R
internal_looper_nomod.R
make_table.R
par_fun.R
par_fun_double.R
par_fun_eset.R
par_fun_eset_double.R
par_fun_fewer.R
par_fun_internal.R
prep_eset_sim.R
simple.tmpl
supplement.pdf
supplement_run.R

README.md

genesigprecision

R Code for reproducing analysis for "Measuring the contribution of genomic predictors through estimator precision gain"

R packages

Required packages: BatchJobs, xtable, genefu, Biobase, rpart

File Description

  • functions.R contains the code necessary for estimator adjustment, as describe in the "Methods" section of the paper.

  • genesigprecision_data.Rda contains the 296-patient MammaPrint validation (Buyse) dataset and the three GEO datasets referred to in the paper

  • par_fun.R contains the parallelized simulation function that computes the adjusted estimators 1000 times. This is run 100 times each via BatchJobs to attain 100,000 total simulations.

  • par_fun_internal.R contains a simulation approach to running 10 subsamplings of the data and resampling 100,000 times from each subsample. This is done to get a sense of the variability of our gain estimates.

  • par_fun_double.R is the same as par_fun.R except we double the sample size when we resample with replacment

  • par_fun_fewer.R is the same as par_fun.R except we use fewer clinical adjustment covariates

  • par_fun_eset.R accomplishes the same task as par_fun.R, except it is tailored to handle ExpressionSet data as input

  • par_fun_eset_double.R is the same as par_fun_eset.R, except we double the sample size when we resample with replacement

  • internal_looper_nomod.R is a helper function to conduct the 10x10,000 simulation.

  • genesigprecision_run.R contains code to run the analysis and reproduce the results in the paper.

  • .BatchJobs.R and simple.tmpl are configuration files necessary for running the parallelized job on a cluster.

  • supplement_run.R contains code used to generate additional results regarding variability of samples and permuatation of labels under larger sample size and fewer covariates.

  • supplement.pdf is a PDF version of the supplementary material

Instructions

NOTE: This analysis relies on a Sun Grid Engine cluster. We provide the configuration files and code necessary to reproduce this analysis on an SGE platform. Please examine the code in genesigprecision_run.R before you run it because it contains the commands to create a registry and submit SGE batch jobs via the BatchJobs R package.

  1. Install the required packages.
  2. Download all files in this github repository and keep them together in a local directory on your cluster.
  3. Run the code from genesigprecision_run.R.
    • We ask that you run this file line-by-line and do not source it directly.
    • Please keep an eye for "submitJobs" commands in this file - these submit the batch jobs and are at the mercy of the cluster.
    • The "waitForJobs" function which is used to monitor the progress of a simulation may error out due to database timeout (see this link for further discussion) To avoid this, please wait a couple minutes before running the command to allow the jobs to start up on your cluster. If an error does occurr, the jobs will still run on the cluster and results will still be returned correctly. If the command does error, you may (1) re-run the command and wait for the database to unlock for monitoring, or (2) periodically check job progress directly on the cluster by using "system('qstat | wc -l')" to count how many jobs are currently running. If the error persits, you can restart R and load all the packages/data again - the jobs will still run and be aggregated by BatchJobs, and you can load the result when they are finished by going into the job directory (e.g., full-results/ for MammaPrint) and loading "registry.RData" before running the next functions, such as "y <- loadResults(reg)".
  4. (Optional) Run the code from supplement_run.R if you would like to recreate the results in the supplement as well. The same caveats about running cluster jobs apply. The code for the doubled sample size runs is in this file.