BUSexample_data: A simulated data set
In XiangyuLuo/BUScorrect: Batch Effects Correction with Unknown Subtypes

A simulated data set for demonstrating how to use the BUScorrect package

## Not run: 
#This data set is simulated according to the following R code
rm(list = ls(all = TRUE))  
set.seed(123456)

B <- 3					
#total number of batches

K <- 3					
#total number of subtypes

G <- 2000					
#total number of genes

pi <- matrix(NA, B, K)			
# pi[b,k] stands for the proportion of kth subtype in bth batch

pi[1, ] <- c(0.2, 0.3, 0.5)
pi[2, ] <- c(0.4, 0.2, 0.4)
pi[3, ] <- c(0.3, 0.4, 0.3)

	
#total number of samples in each bacth.
n_vec <- rep(NA, B) 		
	
#n_vec[b] represents the total number of samples in batch b.
n_vec <- c(70, 80, 70)

#Data list
example_Data <- list()


#baseline expression level
alpha <- rep(2, G)



#subtype effect
mu <- matrix(NA, G, K)			
#subtype effect, mu[g,k] stands for the kth-subtype effect of gene g 

mu[ ,1] <- 0	
#the first subtype is taken as the baseline subtype			
#the subtype effect of subtype 1 is set to zero

mu[ ,2] <- c(rep(2,G/20), rep(0,G/20),rep(0, G-G/20-G/20))
mu[ ,3] <- c(rep(0,G/20), rep(2,G/20),rep(0, G-G/20-G/20)) 

#batch effect
gamma <- matrix(NA, B, G)		
#'location' batch effect of gene g in batch b 
gamma[1, ] <- 0			
#the first batch is taken as the reference batch without batch effects	
#the batch effect of batch 1 is set to zero
gamma[2, ] <- c(rep(3,G/5),rep(2,G/5),rep(1,G/5),
										rep(2,G/5),rep(3,G/5))
gamma[3, ] <- c(rep(1,G/5),rep(2,G/5),rep(3,G/5),
										rep(2,G/5),rep(1,G/5))

sigma_square <- matrix(NA, B,G)	
#sigma_square[b,g] denotes the error variance of gene g in batch b.

sigma_square[1,] <- rep(0.1, G)
sigma_square[2,] <- rep(0.2, G)
sigma_square[3,] <- rep(0.15, G)


Z <- list()			
#subtype indicator. Z[b,j] represents the subtype of sample j in batch b

Z[[1]] <- as.integer(c(rep(1,floor(pi[1,1]*n_vec[1])),rep(2,floor(pi[1,2]*n_vec[1])),
 rep(3,floor(pi[1,3]*n_vec[1]))))	
 
Z[[2]] <- as.integer(c(rep(1,floor(pi[2,1]*n_vec[2])),rep(2,floor(pi[2,2]*n_vec[2])), 
rep(3,floor(pi[2,3]*n_vec[2]))))	

Z[[3]] <- as.integer(c(rep(1,floor(pi[3,1]*n_vec[3])),rep(2,floor(pi[3,2]*n_vec[3])), 
rep(3,floor(pi[3,3]*n_vec[3]))))


for(b in 1:B){				#generate data 
	num <- n_vec[b]
	example_Data[[b]] <-  sapply(1:num, function(j){
						tmp <- alpha + mu[ ,Z[[b]][j]] + gamma[b, ] + 
							rnorm(G, sd = sqrt(sigma_square[b, ])) 
									
						tmp
					}) 

}
BUSexample_data <- example_Data

## End(Not run)