#10th March 2022
#create dataframe to run simulations in the cluster
#each line will be a combination of parameters to run the simulations
# I have 100 replicates using the same combinations of parameter values
# and I will analyse different percentages of the population
# from 5%, 10% to 90%.
params <- c(rep(1067, 1000), rep(2348, 1000))
percentage <- c(rep(0.05, 100), rep(0.1, 100),
rep(0.2, 100), rep(0.3, 100),
rep(0.4, 100), rep(0.5, 100),
rep(0.6, 100), rep(0.7, 100),
rep(0.8, 100), rep(0.9, 100))
replicate <- rep(1:100)
simulations <- data.frame(params = params, perc = rep(percentage, 2), rep = rep(replicate, 20))
saveRDS(simulations, "inst/data/simulatins_imperial_clsuter.RDS")
#15th March 2022
#create dataframe to run simulations in the cluster (XSEDE)
#each line will be a combination of parameters to run the simulations
# I have 100 replicates using the same combinations of parameter values
# and I will analyse different percentages of the population
# from 5%, 10% to 90%.
params <- c(rep(1067, 200), rep(2348, 200))
percentage <- c(rep(0.05, 100), rep(0.3, 100))
replicate <- rep(1:100)
simulations <- data.frame(params = params, perc = rep(percentage, 2),
rep = rep(replicate, 4))
saveRDS(simulations, "inst/data/simulations_deepseq_cluster.RDS")
#test run in order to have very few sequences
params <- c(rep(1067, 2), rep(2348, 2))
percentage <- c(rep(0.01, 4))
replicate <- rep(1:2)
simulations_test <- data.frame(params = params, perc = percentage,
rep = rep(replicate, 2))
saveRDS(simulations, "inst/data/simulations_deepseq_cluster_test.RDS")
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