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simulation.R
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simulation.R
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# Simulation functions
#
# Author: Xuran Wang
##################################################################################
#' Simulate Single cell read counts
#'
#' Simulate expected library sizes from a log-normal distribution
#'
#' @param N integer, number of subjects in total.
#' @param n.bulk integer, number of bulk subjects.
#' @param G integer, number of genes.
#' @param mu matrix, 2 by G, the mean expression of genes in each cell types.
#' @param sub.var matrix, 2 by G, scale of log-normal distribution for each subjects at genes. Part of them are zeros.
#' @param sort.bulk logical, default is FALSE. Sort the cell type proportion.
#'
#' @details
#' Generate simulation datasets. n.sc or n.bulk is zero, then we only generate
#' one dataset and cell type proportions.
#'
#' @return a list with elements
#' \itemize{
#' \item {p.bulk: cell type proportions of generated bulk data;}
#' \item {p.sc: cell type proportions of generated single cell data;}
#' \item {bulk.mtx: Expression Matrix of bulk data;}
#' \item {sc.sce: SingleCellExperiment of single cell data;}
#'}
#' @importFrom MCMCpack rdirichlet
#' @importFrom stats rlnorm
#'
#' @export
Twocelltype.Generator = function(n.sc = 10, n.bulk = 100, G = 100, mu, sub.var, sort.bulk = FALSE, sigma = NULL, ...){
K = 2; #2 cell types
N = n.sc + n.bulk
#generate cell number and cell type proportion
Ni = sample(700:2000, N, replace = T)
p = rdirichlet(N, rep(4, K))
Nk = cbind(round(p[,1]*Ni), round(p[,2]*Ni))
p = Nk/Ni
muA = mu[1, ] #c(rep(0,10), runif(90, 20, 70))
muB = mu[2, ] #c(rep(50, 10), rep(0, 90)) + muA
#sf = sample(1:10, 5); muA[sf] = 50; muB[sf] = 0;
# Generate subject level variation
sub.Fac = list(sapply(1:G, function(i){rlnorm(N, 0, sub.var[1, i])}),
sapply(1:G, function(i){rlnorm(N, 0, sub.var[2, i])}) )
GN = NULL;
for(g in 1:G){
GN = c(GN, paste('gene', g, sep = ''))
}
if(G>9){
GN[1:9] = c('gene01', 'gene02', 'gene03', 'gene04', 'gene05', 'gene06', 'gene07', 'gene08', 'gene09');
}else{
GN[1:G] = paste0('gene0', 1:G)
}
Bulk.null = FALSE; SC.null = FALSE;
if(n.bulk == 0) Bulk.null = TRUE;
if(n.sc == 0) SC.null = TRUE;
if(is.null(sigma)){
if(!Bulk.null){
Xjg = NULL # generate bulk tissue data
for(i in 1:n.bulk){
Xjg = cbind( Xjg, colSums( sapply(muA*sub.Fac[[1]][i, ], rpois, n = Nk[i,1]) )
+ colSums( sapply(muB*sub.Fac[[2]][i, ], rpois, n = Nk[i,2]) ) )
}
}
if(!SC.null){
nk = NULL; #S = NULL; thetaA = NULL; thetaB = NULL; # generate single cell statistics
Xjgc = NULL; #Xsc.cov = NULL# generate single cell data
for(i in (1+n.bulk):N){
nk1 = max(rbinom(1, Nk[i, 1], 0.1), 2)
Xjgc1 = t( sapply(muA*sub.Fac[[1]][i, ], rpois, n = nk1) )
#thetaA = cbind(thetaA, rowSums(Xjgc1)/sum(Xjgc1))
nk2 = max(rbinom(1, Nk[i, 2], 0.1), 2)
Xjgc2 =t( sapply(muB*sub.Fac[[2]][i, ], rpois, n = nk2) )
#thetaB = cbind(thetaB, rowSums(Xjgc2)/sum(Xjgc2))
#S =rbind(S, c( mean( colSums(Xjgc1)), mean( colSums(Xjgc2))) )
nk = rbind(nk, c(nk1, nk2));
Xjgc = cbind(Xjgc, Xjgc1, Xjgc2);
#Xsc.cov = cbind(Xsc.cov, rbind(rep(paste('indv', i-100, sep = '')), c(rep('A', nk1), rep('B', nk2) )) )
}
}
}else{
lambdaA = sapply(sigma/muA, rgamma, n = N, shape = sigma)
lambdaB = sapply(sigma/muB, rgamma, n = N, shape = sigma)
if(!Bulk.null){
Xjg = NULL # generate bulk tissue data
for(i in 1:n.bulk){
Xjg = cbind( Xjg, colSums( sapply(lambdaA[i, ] * sub.Fac[[1]][i, ], rpois, n = Nk[i,1]) )
+ colSums( sapply(lambdaB[i, ] * sub.Fac[[2]][i, ], rpois, n = Nk[i,2]) ) )
}
}
if(!SC.null){
nk = NULL; #S = NULL; thetaA = NULL; thetaB = NULL; # generate single cell statistics
Xjgc = NULL; #Xsc.cov = NULL# generate single cell data
for(i in (1+n.bulk):N){
nk1 = max(rbinom(1, Nk[i, 1], 0.1), 2)
Xjgc1 = t( sapply(lambdaA[i, ] * sub.Fac[[1]][i, ], rpois, n = nk1) )
#thetaA = cbind(thetaA, rowSums(Xjgc1)/sum(Xjgc1))
nk2 = max(rbinom(1, Nk[i, 2], 0.1), 2)
Xjgc2 =t( sapply(lambdaB[i, ]*sub.Fac[[2]][i, ], rpois, n = nk2) )
#thetaB = cbind(thetaB, rowSums(Xjgc2)/sum(Xjgc2))
#S =rbind(S, c( mean( colSums(Xjgc1)), mean( colSums(Xjgc2))) )
nk = rbind(nk, c(nk1, nk2));
Xjgc = cbind(Xjgc, Xjgc1, Xjgc2);
#Xsc.cov = cbind(Xsc.cov, rbind(rep(paste('indv', i-100, sep = '')), c(rep('A', nk1), rep('B', nk2) )) )
}
}
}
if(!SC.null){
stop.idx = cumsum( rowSums(nk) )
start.idx = c(1, stop.idx[-(N-n.bulk)]+1)
idx.level = unlist(sapply(1:(N-n.bulk), function(i){rep(i, rowSums(nk)[i])}))
ct.level = unlist(sapply(1:(N-n.bulk), function(i){c(rep(1, nk[i,1]), rep(2, nk[i, 2]))}))
}
if(!Bulk.null){
colnames(p) = c('A', 'B');
p.bulk = p[1:n.bulk, ];
if(sort.bulk){
op.bulk = order(p.bulk[,1])
p.bulk = p.bulk[op.bulk, ]; rownames(p.bulk) = paste0('Bulk', 1:n.bulk)
Xjg = Xjg[, op.bulk]; colnames(Xjg) <- paste0('Bulk', 1:n.bulk)
}else{
rownames(p.bulk) = paste0('Bulk', 1:n.bulk)
colnames(Xjg) <- paste0('Bulk', 1:n.bulk)
}
rownames(Xjg) <- GN;
bulk.mtx = data.matrix(Xjg)
}
if(!SC.null){
p.sc = p[(n.bulk+1):N, ]; rownames(p.sc) = paste0("Sub", (n.bulk+1):N);
rownames(Xjgc) <- GN
colnames(Xjgc) <- paste0('cell', 1:ncol(Xjgc))
SC.pData = data.frame(sampleID = ((n.bulk+1):N)[idx.level], SubjectName = factor(paste0("Sub", (n.bulk+1):N)[idx.level], levels = paste0("Sub", (n.bulk+1):N)),
cellTypeID = ct.level, cellType = c('A', 'B')[ct.level])
rownames(SC.pData) = colnames(Xjgc)
sc.sce = SingleCellExperiment(list(counts = data.matrix(Xjgc)), colData = SC.pData)
}
if(SC.null){
p.sc = NULL; sc.sce = NULL;
}
if(Bulk.null){
p.bulk = NULL; bulk.mtx = NULL;
}
out = list(p.bulk = p.bulk, p.sc = p.sc, bulk.mtx = bulk.mtx, sc.sce = sc.sce)
return(out)
}
#' Evaluate simulation results by boxplots
#'
Eval_sim_boxplot = function(list, title.sup = ''){
par(mfrow = c(3, 1))
boxplot(t(list$R.sim), main = paste0('Boxplot of Pearson Correlation, ', title.sup))
lines(c(-10, 100), rep(max(apply(list$R.sim, 1 , median)), 2), col = 2 )
boxplot(t(list$RMSD.sim), main = paste0('Boxplot of RMSD, ', title.sup))
lines(c(-10, 100), rep(min(apply(list$RMSD.sim, 1 , median)), 2), col = 2 )
boxplot(t(list$mAD.sim), main = paste0('Boxplot of mAD, ', title.sup))
lines(c(-10, 100), rep(min(apply(list$mAD.sim, 1 , median)), 2), col = 2 )
}