01_simulate_data.R

# This script will simulate some microscopy analysis data and
# format it so that it resembles the ImageJ measurement window
# i.e. the typical output from a cell biology experiment

## variables ----
set.seed(123)
filerows<-20
files<-10

## functions ----
generate_df<-function (area=1248, meas=100, rows=filerows) {
df<-data.frame(
Area= rep(area, rows),
Mean= rnorm(rows, mean=meas, sd=meas/5),
StdDev= rnorm(rows, mean=meas/5, sd=2),
Min= rep(0, rows),
Max= rep(255, rows)
 )
df$IntDen<- floor(df$Mean*df$Area)
df$RawIntDen<- floor(df$Mean*df$Area)
return(df)
}

make_csv<-function (files=files, fname="file", measure=100) {
for (iin1:files) {
df<- generate_df(meas=measure)
 write.csv(df, file= paste0("Output/Data/", fname, "_", i, ".csv"))
 }
}

## generate data ---
# we will make 10 files for each of the control and rapamycin treated groups
# each file will have 20 rows of data (cells)
# the mean intensity will be different for each group
# we will make four sets that mimic four experimental repeats
# n4 is simulated to be an experiment where the treatment didn't work well
make_csv(files=files, fname="control_n1", measure=55)
make_csv(files=files, fname="control_n2", measure=52)
make_csv(files=files, fname="control_n3", measure=48)
make_csv(files=files, fname="control_n4", measure=50)
make_csv(files=files, fname="rapa_n1", measure=192)
make_csv(files=files, fname="rapa_n2", measure=202)
make_csv(files=files, fname="rapa_n3", measure=180)
make_csv(files=files, fname="rapa_n4", measure=75)