README.md

In mmariani123/MethylMasteR: What the Package Does (One Line, Title Case)

MethylMasteR

Michael Mariani PhD, Salas Lab, Dartmouth College 2022

For citation see: https://www.frontiersin.org/articles/10.3389/fbinf.2022.859828/full

1.) install Docker on your OS of choice 2.) Then run: docker pull mmariani123/methylmaster:latest 3.) Then start the container with local_path set to the desired output directory e.g.: user_name= local_path="C:\Users\$user_name\Desktop\rocker_test" docker run --rm -v $local_path:/home/rstudio -p 127.0.0.1:8787:8787 -e DISABLE_AUTH=true mmariani123/methylmaster 4.) Open your web browser of choice and navigate to http://127.0.0.1:8787/ 5.) Follow the commands below in the Rocker RStudio session ... (skip down to the "Run MethylMasteR" section)

Install command line version for use on Slurm HPC:

***If MethylMasteR singularity image (".sif" file") has already been created, skip to the next section

1.) Make sure that Singularity is installed on your cluster 2.) Then run: "singularity pull methylmaster_base_slim_script.sif docker://mmariani123/methylmaster_base_slim_script:latest"" This will pull the Docker container and create a singularity container file (".sif") (Note: you can change the name of "methylmaster_base_slim_script.sif" to anything that you want)

Run command line version for use on Slurm HPC:

***For an example working directory with test idat files, sample sheet, run_methylmaster.R, and Slurm bash script all in the same folder, see the "example" folder above.

1.) create a working directory on your HPC where you would like to run MethylMasteR e.g. "mkdir /dartfs/rc/lab/S/SalasLab/programs/methylmaster_testing" (Note: you can name the folder whatever you like) 2.) Create an R script using the example R code below in the "Run MethylMasteR" section You can adjust your own parameters as desired but MAKE SURE to name the R file: "run_methylmaster.R". Place the "run_methylmaster.R" file in the working directory you created above. 3.) Then, include the following command in a .bash file (name it whatever you like) and submit this bash file on your Slurm HPC /usr/bin/singularity run \ -B /dartfs/rc/lab/S/SalasLab/programs/methylmaster_testing:/home/docker/work \ /dartfs/rc/lab/S/SalasLab/programs/methylmaster_base_slim_script.sif (Note: make sure that "/dartfs/rc/lab/S/SalasLab/programs/methylmaster_testing" is set to your working directory (remember it does not have to be called "methylmaster_testing" but DO NOT change "/home/docker/work" because this path is set inside the container. Also, make sure that "/dartfs/rc/lab/S/SalasLab/programs/methylmaster_base_slim_script.sif" is the path to the .sif file that was created) 4.) Files will be output to the working directory that you created above

Run MethylMasteR

Troubleshooting SeSAMe data caching

If you are having SeSAMe caching issues, running the function below in R should solve the issue https://bioconductor.org/packages/devel/bioc/vignettes/ExperimentHub/inst/doc/ExperimentHub.html#default-caching-location-update

moveFiles<-function(package){
olddir <- path.expand(rappdirs::user_cache_dir(appname=package))
newdir <- tools::R_user_dir(package, which="cache")
dir.create(path=newdir, recursive=TRUE)
files <- list.files(olddir, full.names =TRUE)
moveres <- vapply(files,
    FUN=function(fl){
    filename = basename(fl)
    newname = file.path(newdir, filename)
    file.rename(fl, newname)
    },
    FUN.VALUE = logical(1))
if(all(moveres)) unlink(olddir, recursive=TRUE)
}
package="ExperimentHub"
moveFiles(package)

If the above function doesn't work, try caching the sesameData directly

library(sesameData)
sesameDataCacheAll()

Load the test data:

library(MethylMasteR)

input.dir <- system.file("extdata",
              package = 'MethylMasteR')

#Create an output dir for the SeSAMe test data:
output.dir <- paste0(getwd(),.Platform$file.sep,"sesame")

sample.sheet.path <- system.file("extdata",
                   "Sample_Sheet_Test.csv",
                   package = 'MethylMasteR')

Select your routine:

Set ="sesame" to try the sesame routine with the test data specified above.

routine.run <- "sesame"
#1.) "sesame" for SeSAMEe
#2.) "hm450" for HM450
#3.) "champ" for ChAMP
#4.) "epicopy" for Epicopy
#5.) "custom" for custom routine

Select the other parameters and run!

Note that it is recommended to set to c(-0.2,0.2) when running the test data specified above.

methyl_master(
  routine                   = routine.run, #The routine to run
  input.dir                 = input.dir, #The input (idat.files) directory
  output.dir                = output.dir, #The output directory
  sample.sheet.path         = sample.sheet.path, #The path to the MethylMasteR sample sheet
  r.lib.path                = .libPaths()[1], #The path to the R Library path
  file.sep                  = "\\\\", #For windows or "/" for Linux
  create.dir                = TRUE, #Whether to cretae directory if does not 
                                    #exist?
  save.seg                  = TRUE, #Whether to save segmentation results
  n.cores                   = 1, #Multicore does not work for all routines 
                                 #on all operating systems
  os.type                   = "windows", #Or "linux"
  proj                      = "TCGA-KIRC", #"TCGA-BLCA" etc.
  visualize                 = TRUE, #Whether to output plots,
  visualize.individual      = FALSE, #Whether to output indvidual sample plots,
                                     #only works for routine sesame
  reference                 = "internal", #"comparison" or 'internal"
  reference.name            = NA, #For Epicopy use NA for median, 
                                  #"all" is not currently supported 
                                  #by Epicopy
  comparison                = c("tumor","normal"), #Always required treatment 
                              #first then contro can also be more specific when 
                              #designing sample sheet and use values like
                              # c("tumor_male","cord_male etc") etc.
                              #Note: in routines where internal reference is 
                              #used, second argument is ignored
  form.thresholds           = NULL, #Used to calculate final CNV state. 
                                    #If NULL, equation is is used;
                                    #otherwise, specify threshold vector 
                                    #of lower and upper beta values such
                                    #as c(-0.3,0.3), we used c(-0.2,0.2) 
                                    #in the paper
  overlap.density           = 0.1, #For combining final CNV calls for confidence
  sesame.data.cache         = "EPIC", #The default sesame reference platform
  sesame.data.normal        = 'EPIC.5.normal', #The default sesame and hm450 
                                               #internal reference samples
  genome.version            = "hg19", #Or can set to "hg38"
  hm450.workflow            = "B", #The HM450 subworkflow to use - only B 
                                   #is running currently.
                              #"A" no correction,
                              #"B" median correction (default),
                              #"C" run Conumee
  champ.padj                = 0.05, #padj to filter champ results
  champ.control             = FALSE, #run champ.control etc.
  champ.run.combat          = FALSE, #run champ.run.combat etc.
  champ.run.dmp             = FALSE, #If only one pheno var must = FALSE
  champ.run.dmr             = FALSE, #If only one pheno var must = FALSE
  champ.run.block           = FALSE, #If only one pheno var must = FALSE
  champ.run.gsea            = FALSE, #Requires dmp and dmr results
  champ.run.epimod          = FALSE, #If only one pheno var must = FALSE
  epi.run.gistic            = TRUE, #Whether to Run GISTIC in Epicopy workflow
  olaps.split.field         = "Sample_ID", #Split field to ise during overlaps
                                           #Don't change unless you know what 
                                           #you are doing
  estimate.recurrence       = TRUE, #Estimate recursion to produce p values when 
                                    #finding overlaps with population_ranges 
                                    #functions
  ov.pvalue                 = 0.05, #pvalue threshold for overlaps identified
  ov.keep.extra.columns     = TRUE, #Keep extra metadata columns when finding 
                                    #overlaps
  simplify.reduce           = weightedmean #Equation to use during reduction
)

mmariani123/MethylMasteR documentation built on June 22, 2022, 3:06 p.m.