data-raw/create_data.R
In andreaskapou/BPRMeth-devel: Model higher-order methylation profiles
create_meth_data <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
X <- list()
# A vector for storing corresponding gene expression data
Y <- 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[[i]] <- matrix(0, nrow = L, ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L))
# Scale them, so the data lie in the (fmin, fmax) range
X[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L / 4)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X[[i]][(lb + 1):L,3] <- rbinom(L - lb, 2, .9)
if (all(X[[i]][(lb + 1):L,2] > X[[i]][(lb + 1):L,3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L / 1.5)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X[[i]][(lb + 1):L,3] <- rbinom(L-lb, 14, .9)
if (all(X[[i]][(lb + 1):L,2] > X[[i]][(lb + 1):L,3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L / 2.5)
mb <- round(L / 3.5)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 2, .9)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):(lb + mb),2] <- rbinom(mb, 20, .9)
repeat{
X[[i]][(lb + 1):(lb + mb),3] <- rbinom(mb, 14, .9)
if (all(X[[i]][(lb + 1):(lb + mb),2] > X[[i]][(lb + 1):(lb + mb),3]))
break
}
X[[i]][(lb + 1 + mb):L,2] <- rbinom(L - mb - lb, 20, .9)
repeat{
X[[i]][(lb + 1 + mb):L,3] <- rbinom(L - mb - lb, 2, .9)
if (all(X[[i]][(lb + 1 + mb):L,2] > X[[i]][(lb + 1 + mb):L],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
set.seed(1)
bpr <- create_meth_data(N=600)
meth_data <- bpr$X
gex_data <- bpr$Y
devtools::use_data(meth_data, gex_data, overwrite = TRUE)
create_lm_data <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
true_w = list(w1 = c(-1.1, -2, 2.7, -2),
w2 = c(2, 1, -2, 1),
w3 = c(0.4, -0.7, 1.7, 2.8)),
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(123)
# Create a list to store data for each methylation region
X <- list()
# 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[[i]] <- matrix(0, nrow = L, ncol = 2)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L))
# Scale them, so the data lie in the (fmin, fmax) range
X[[i]][, 1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
# Choose function to generate the data
if (i < N * pi.c[1])
cl_label <- 1
else if (i < (N * pi.c[2] + N * pi.c[1]))
cl_label <- 2
else
cl_label <- 3
# Randomly sample a cluster to generate data from
# cl_label <- sample(x = 3, size = 1, prob = pi.c)
# Set corresponding weights
w <- true_w[[cl_label]]
# Create basis function object
basis <- create_rbf_object(M = 3)
# Create design matrix
H <- BPRMeth::design_matrix(basis, X[[i]][ ,1])$H
# Compute mean of data
mu <- H %*% w
# Randomly generate data from this function
X[[i]][, 2] <- rnorm(L, mu, 0.1)
}
return(X)
}
set.seed(1)
lm_out <- create_lm_data(N=600)
lm_data <- lm_out
devtools::use_data(lm_data, overwrite = TRUE)
create_meth_data_2 <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
#X <- list()
# A vector for storing corresponding gene expression data
Y <- vector(mode = "numeric", length = N)
X <- list(control = list(), treatment = list())
L <- list()
for (i in 1:N){
# L is the number of CpGs found in the ith region
L[[i]] <- rbinom(n = 1, size = max_L, prob = .8)
}
# For each of the N objects
for (i in 1:N){
# L is the number of CpGs found in the ith region
#L <- rbinom(n = 1, size = max_L, prob = .8)
X$control[[i]] <- matrix(0, nrow = L[[i]], ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L[[i]]))
# Scale them, so the data lie in the (fmin, fmax) range
X$control[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
X$treatment[[i]] <- X$control[[i]]
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$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[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$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[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
X$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[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$control[[i]][(lb + 1 + mb):L[[i]],2] > X$control[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
X$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[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1 + mb):L[[i]],2] > X$treatment[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
create_meth_data_3 <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
#X <- list()
# A vector for storing corresponding gene expression data
Y <- vector(mode = "numeric", length = N)
X <- list(control = list(), treatment = list())
L <- list()
for (i in 1:N){
# L is the number of CpGs found in the ith region
L[[i]] <- rbinom(n = 1, size = max_L, prob = .8)
}
# For each of the N objects
for (i in 1:N){
# L is the number of CpGs found in the ith region
#L <- rbinom(n = 1, size = max_L, prob = .8)
X$control[[i]] <- matrix(0, nrow = L[[i]], ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L[[i]]))
# Scale them, so the data lie in the (fmin, fmax) range
X$control[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
X$treatment[[i]] <- X$control[[i]]
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$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[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$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[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
X$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[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$control[[i]][(lb + 1 + mb):L[[i]],2] > X$control[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 8, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
X$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[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1 + mb):L[[i]],2] > X$treatment[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
andreaskapou/BPRMeth-devel documentation built on May 12, 2019, 3:32 a.m.
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create_meth_data <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
X <- list()
# A vector for storing corresponding gene expression data
Y <- 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[[i]] <- matrix(0, nrow = L, ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L))
# Scale them, so the data lie in the (fmin, fmax) range
X[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L / 4)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X[[i]][(lb + 1):L,3] <- rbinom(L - lb, 2, .9)
if (all(X[[i]][(lb + 1):L,2] > X[[i]][(lb + 1):L,3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L / 1.5)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):L,2] <- rbinom(L - lb, 20, .9)
repeat{
X[[i]][(lb + 1):L,3] <- rbinom(L-lb, 14, .9)
if (all(X[[i]][(lb + 1):L,2] > X[[i]][(lb + 1):L,3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L / 2.5)
mb <- round(L / 3.5)
X[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X[[i]][1:lb,3] <- rbinom(lb, 2, .9)
if(all(X[[i]][1:lb,2] > X[[i]][1:lb,3]))
break
}
X[[i]][(lb + 1):(lb + mb),2] <- rbinom(mb, 20, .9)
repeat{
X[[i]][(lb + 1):(lb + mb),3] <- rbinom(mb, 14, .9)
if (all(X[[i]][(lb + 1):(lb + mb),2] > X[[i]][(lb + 1):(lb + mb),3]))
break
}
X[[i]][(lb + 1 + mb):L,2] <- rbinom(L - mb - lb, 20, .9)
repeat{
X[[i]][(lb + 1 + mb):L,3] <- rbinom(L - mb - lb, 2, .9)
if (all(X[[i]][(lb + 1 + mb):L,2] > X[[i]][(lb + 1 + mb):L],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
set.seed(1)
bpr <- create_meth_data(N=600)
meth_data <- bpr$X
gex_data <- bpr$Y
devtools::use_data(meth_data, gex_data, overwrite = TRUE)
create_lm_data <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
true_w = list(w1 = c(-1.1, -2, 2.7, -2),
w2 = c(2, 1, -2, 1),
w3 = c(0.4, -0.7, 1.7, 2.8)),
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(123)
# Create a list to store data for each methylation region
X <- list()
# For each of the N objects
for (i in 1:N){
# L is the number of CpGs found in the ith region
L <- rbinom(n = 1, size = max_L, prob = .8)
X[[i]] <- matrix(0, nrow = L, ncol = 2)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L))
# Scale them, so the data lie in the (fmin, fmax) range
X[[i]][, 1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
# Choose function to generate the data
if (i < N * pi.c[1])
cl_label <- 1
else if (i < (N * pi.c[2] + N * pi.c[1]))
cl_label <- 2
else
cl_label <- 3
# Randomly sample a cluster to generate data from
# cl_label <- sample(x = 3, size = 1, prob = pi.c)
# Set corresponding weights
w <- true_w[[cl_label]]
# Create basis function object
basis <- create_rbf_object(M = 3)
# Create design matrix
H <- BPRMeth::design_matrix(basis, X[[i]][ ,1])$H
# Compute mean of data
mu <- H %*% w
# Randomly generate data from this function
X[[i]][, 2] <- rnorm(L, mu, 0.1)
}
return(X)
}
set.seed(1)
lm_out <- create_lm_data(N=600)
lm_data <- lm_out
devtools::use_data(lm_data, overwrite = TRUE)
create_meth_data_2 <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
#X <- list()
# A vector for storing corresponding gene expression data
Y <- vector(mode = "numeric", length = N)
X <- list(control = list(), treatment = list())
L <- list()
for (i in 1:N){
# L is the number of CpGs found in the ith region
L[[i]] <- rbinom(n = 1, size = max_L, prob = .8)
}
# 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$control[[i]] <- matrix(0, nrow = L[[i]], ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L[[i]]))
# Scale them, so the data lie in the (fmin, fmax) range
X$control[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
X$treatment[[i]] <- X$control[[i]]
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
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$control[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
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$control[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
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$control[[i]][(lb + 1 + mb):L[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$control[[i]][(lb + 1 + mb):L[[i]],2] > X$control[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
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$treatment[[i]][(lb + 1 + mb):L[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1 + mb):L[[i]],2] > X$treatment[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
create_meth_data_3 <- function(N = 300, pi.c = c(0.45, 0.35, 0.2), max_L = 25,
xmin = -100, xmax=100, fmin = -1, fmax = 1){
set.seed(3)
# Create a list to store data for each methylation region
#X <- list()
# A vector for storing corresponding gene expression data
Y <- vector(mode = "numeric", length = N)
X <- list(control = list(), treatment = list())
L <- list()
for (i in 1:N){
# L is the number of CpGs found in the ith region
L[[i]] <- rbinom(n = 1, size = max_L, prob = .8)
}
# 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$control[[i]] <- matrix(0, nrow = L[[i]], ncol = 3)
# Randomly sample locations for the CpGs
obs <- sort(sample(xmin:xmax, L[[i]]))
# Scale them, so the data lie in the (fmin, fmax) range
X$control[[i]][ ,1] <- BPRMeth:::.minmax_scaling(data = obs,
xmin = xmin,
xmax = xmax,
fmin = fmin,
fmax = fmax)
X$treatment[[i]] <- X$control[[i]]
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
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$control[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 2, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
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$control[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$control[[i]][(lb + 1):L[[i]],2] > X$control[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
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$control[[i]][(lb + 1 + mb):L[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$control[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$control[[i]][(lb + 1 + mb):L[[i]],2] > X$control[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
for (i in 1:N){
if (i < N * pi.c[1]){ # First methylation profile
lb <- round(L[[i]] / 4)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 14, .9)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]] - lb, 8, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=200)
}else if (i < (N * pi.c[2] + N * pi.c[1])){ # Second methylation profile
lb <- round(L[[i]] / 1.5)
X$treatment[[i]][1:lb,2] <- rbinom(lb, 20, .9)
repeat{
X$treatment[[i]][1:lb,3] <- rbinom(lb, 2, .8)
if(all(X$treatment[[i]][1:lb,2] > X$treatment[[i]][1:lb,3]))
break
}
X$treatment[[i]][(lb + 1):L[[i]],2] <- rbinom(L[[i]] - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1):L[[i]],3] <- rbinom(L[[i]]-lb, 14, .9)
if (all(X$treatment[[i]][(lb + 1):L[[i]],2] > X$treatment[[i]][(lb + 1):L[[i]],3]))
break
}
Y[i] <- rpois(1, lambda=100)
}else{ # Third methylation profile
lb <- round(L[[i]] / 2.5)
mb <- round(L[[i]] / 3.5)
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$treatment[[i]][(lb + 1 + mb):L[[i]],2] <- rbinom(L[[i]] - mb - lb, 20, .9)
repeat{
X$treatment[[i]][(lb + 1 + mb):L[[i]],3] <- rbinom(L[[i]] - mb - lb, 2, .9)
if (all(X$treatment[[i]][(lb + 1 + mb):L[[i]],2] > X$treatment[[i]][(lb + 1 + mb):L[[i]]],3))
break
}
Y[i] <- rpois(1, lambda=50)
}
}
return(list(X = X, Y = Y))
}
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