create_diff_bpr_data <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 30,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
# Create a list to store data for each methylation region for both control and
# treated samples
X <- list(control = list(),
treatment = list())
# A list for storing corresponding gene expression data for both control and
# treated samples
Y <- list(control = vector(mode = "numeric", length = N),
treatment = vector(mode = "numeric", length = N))
# For each of the N objects
for (i in 1:N){
# L is the number of CpGs found in the ith region
L <- rbinom(n = 1, size = max_L, prob = .8)
X$control[[i]] <- matrix(0, nrow = L, ncol = 3)
X$treatment[[i]] <- X$control[[i]]
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L))
# Scale them, so the data lie in the (fmin, fmax) range
X$control[[i]][ ,1] <- minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
X$treatment[[i]][ ,1] <- X$control[[i]][ ,1]
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L / 4)
# Control
X$control[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$control[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X$control[[i]][1:lb,2] > X$control[[i]][1:lb,3]))
break
}
X$control[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L,3] <- rbinom(L - lb, 2, .9)
if (all(X$control[[i]][(lb + 1):L,2] > X$control[[i]][(lb + 1):L,3]))
break
}
Y$control[i] <- rpois(1, lambda=200)
# Treatment
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L,3] <- rbinom(L - lb, 10, .9)
if (all(X$treatment[[i]][(lb + 1):L,2] > X$treatment[[i]][(lb + 1):L,3]))
break
}
Y$treatment[i] <- rpois(1, lambda=100)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L / 1.5)
# Control
X$control[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$control[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X$control[[i]][1:lb,2] > X$control[[i]][1:lb,3]))
break
}
X$control[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L,3] <- rbinom(L-lb, 14, .9)
if (all(X$control[[i]][(lb + 1):L,2] > X$control[[i]][(lb + 1):L,3]))
break
}
Y$control[i] <- rpois(1, lambda=100)
# Treatment
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 12, .8)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L,3] <- rbinom(L-lb, 4, .9)
if (all(X$treatment[[i]][(lb + 1):L,2] > X$treatment[[i]][(lb + 1):L,3]))
break
}
Y$treatment[i] <- rpois(1, lambda=200)
}else{ # Third methylation profile
lb <- round(L / 2.5)
mb <- round(L / 3.5)
# Control
X$control[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$control[[i]][1:lb,3] <- rbinom(lb, 2, .9)
if(all(X$control[[i]][1:lb,2] > X$control[[i]][1:lb,3]))
break
}
X$control[[i]][(lb + 1):(lb + mb),2] <- rbinom(mb, 20, .9)
repeat{
X$control[[i]][(lb + 1):(lb + mb),3] <- rbinom(mb, 14, .9)
if (all(X$control[[i]][(lb + 1):(lb + mb),2] > X$control[[i]][(lb + 1):(lb + mb),3]))
break
}
X$control[[i]][(lb + 1 + mb):L,2] <- rbinom(L - mb - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1 + mb):L,3] <- rbinom(L - mb - lb, 2, .9)
if (all(X$control[[i]][(lb + 1 + mb):L,2] > X$control[[i]][(lb + 1 + mb):L],3))
break
}
Y$control[i] <- rpois(1, lambda=60)
# Treatment
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):(lb + mb),2] <- rbinom(mb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):(lb + mb),3] <- rbinom(mb, 14, .9)
if (all(X$treatment[[i]][(lb + 1):(lb + mb),2] > X$treatment[[i]][(lb + 1):(lb + mb),3]))
break
}
X$treatment[[i]][(lb + 1 + mb):L,2] <- rbinom(L - mb - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1 + mb):L,3] <- rbinom(L - mb - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1 + mb):L,2] > X$treatment[[i]][(lb + 1 + mb):L],3))
break
}
Y$treatment[i] <- rpois(1, lambda=60)
}
}
return(list(X = X, Y = Y))
}
set.seed(1)
bpr <- create_diff_bpr_data(N=600)
bpr_control_data <- bpr$X$control
bpr_treatment_data <- bpr$X$treatment
gex_control_data <- bpr$Y$control
gex_treatment_data <- bpr$Y$treatment
devtools::use_data(bpr_control_data,
bpr_treatment_data,
gex_control_data,
gex_treatment_data, overwrite = TRUE)
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