library(pgxsim)
library(tidyverse)
set.seed(10001)
#simulate cell lines
cell_lines_small <- sim_cell_lines(n=10, type='d', prop=0.2)[,1:2]
cell_lines_large <- sim_cell_lines(n=100, type='d', prop=0.2)[,1:2]
#simulate pIC50 values
pIC50_data_small <- dplyr::mutate(cell_lines_small,
pIC50=sim_pIC50(0,1,beta=1, gene, n=nrow(cell_lines_small), type='d'))
pIC50_data_large <- dplyr::mutate(cell_lines_large,
pIC50=sim_pIC50(0,1,beta=1, gene, n=nrow(cell_lines_large), type='d'))
#simulate dose response data
drc_data_small <- dplyr::mutate(pIC50_data_small,
data=purrr::map(pIC50, ~sim_dose_response(pIC50=., minconc=0.001, maxconc=30,
ndoses=10, nreps=3, sd_prop=0.1, sd_add=0.1)))
drc_data_small <- tidyr::unnest(drc_data_small)
drc_data_large <- dplyr::mutate(pIC50_data_large,
data=purrr::map(pIC50, ~sim_dose_response(pIC50=., minconc=0.001, maxconc=30,
ndoses=10, nreps=3, sd_prop=0.1, sd_add=0.1)))
drc_data_large <- tidyr::unnest(drc_data_large)
#data frame to define simulation
sim_ex1_df <- data_frame(type='discrete', mu=0, lb=-4, ub=Inf, sd=1,
minconc=0.001, maxconc=30, ndoses=10, nreps=3,
sd_prop=0.3, sd_add=0.15, n=50, beta=1, prop=0.5,
sim_unique_id=1:3)
#data frame of data for sim_ex1_df
set.seed(10001)
sim_ex1_data <- do_simulation_type2(sim_ex1_df, output='data')
#make example datasets
devtools::use_data(cell_lines_small,
cell_lines_large,
pIC50_data_small,
pIC50_data_large,
drc_data_small,
drc_data_large,
sim_ex1_df,
sim_ex1_data,
overwrite=TRUE)
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