## module load R/3.4.3
##install
if(!dir.exists("~/.RlibDGCA")){dir.create("~/.RlibDGCA")}
source("https://bioconductor.org/biocLite.R")
biocLite(suppressUpdates=T)
withr::with_libpaths("~/.RlibDGCA",devtools::install_github("nosarcasm/DGCA"))
##load
.libPaths(new="~/.RlibDGCA") ## create this directory and install DGCA to it
library("DGCA",lib="~/.RlibDGCA")
options(stringsAsFactors = FALSE)
message("DGCA batch tools package! hooray!")
message("loading data")
message(Sys.time())
data(darmanis)
data(design_mat)
######################
set.seed(12345)
split = 2 #number of times to split the input. This means (split**2-split)/2 comparisons will be run (see below)
nPerms = 15 #permutations to run
num_cores = 8 #cores per worker
outputfile = "dgca_test_2split_15perm_8c_output.batchtools.txt" #output file (TSV)
input_data = darmanis #input dataframe
design_mat = design_mat #input design matrix
groups = c("oligodendrocyte", "neuron") #groups
corrType="spearman" #correlation type (pearson, spearman, bicor, mutualinformation)
walltime = 90 #walltime in minutes for batch jobs
memory = 3800 #memory per core (MB) for batch jobs
######################
##for multithreaded operation on single machine
cl = parallel::makeCluster(num_cores)
doParallel::registerDoParallel(cl)
##single machine mode - multithreaded
ddcor_res = ddcorAll(input_data, design_mat, groups,
corrType="spearman",adjust="perm",
nPerms=nPerms,cl=cl)
## for batch parallel submission mode - writes output file automatically
ddcorAllParallel(input_data, design_mat, groups, outputfile,
sigOutput=TRUE,corrType="spearman",adjust="perm",
nPerms=nPerms,verbose=TRUE,perBatch=split,
coresPerJob=num_cores,testJob=F,
memPerJob = memory,timePerJob=walltime, batchDir="batchminimal",
batchConfig = "config/batchConfig_Local.R")
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