Created
May 20, 2020 19:06
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type = c(rep("Tumor", 10), rep("Control", 10)) | |
set.seed(888) | |
###################################### | |
# generate methylation matrix | |
rand_meth = function(k, mean) { | |
(runif(k) - 0.5)*min(c(1-mean), mean) + mean | |
} | |
mean_meth = c(rand_meth(300, 0.3), rand_meth(700, 0.7)) | |
mat_meth = as.data.frame(lapply(mean_meth, function(m) { | |
if(m < 0.3) { | |
c(rand_meth(10, m), rand_meth(10, m + 0.2)) | |
} else if(m > 0.7) { | |
c(rand_meth(10, m), rand_meth(10, m - 0.2)) | |
} else { | |
c(rand_meth(10, m), rand_meth(10, m + sample(c(1, -1), 1)*0.2)) | |
} | |
})) | |
mat_meth = t(mat_meth) | |
rownames(mat_meth) = NULL | |
colnames(mat_meth) = paste0("sample", 1:20) | |
###################################### | |
# generate directions for methylation | |
direction = rowMeans(mat_meth[, 1:10]) - rowMeans(mat_meth[, 11:20]) | |
direction = ifelse(direction > 0, "hyper", "hypo") | |
####################################### | |
# generate expression matrix | |
mat_expr = t(apply(mat_meth, 1, function(x) { | |
x = x + rnorm(length(x), sd = (runif(1)-0.5)*0.4 + 0.5) | |
-scale(x) | |
})) | |
dimnames(mat_expr) = dimnames(mat_meth) | |
############################################################# | |
# matrix for correlation between methylation and expression | |
cor_pvalue = -log10(sapply(seq_len(nrow(mat_meth)), function(i) { | |
cor.test(mat_meth[i, ], mat_expr[i, ])$p.value | |
})) | |
##################################################### | |
# matrix for types of genes | |
gene_type = sample(c("protein_coding", "lincRNA", "microRNA", "psedo-gene", "others"), | |
nrow(mat_meth), replace = TRUE, prob = c(6, 1, 1, 1, 1)) | |
################################################# | |
# annotation to genes | |
anno_gene = sapply(mean_meth, function(m) { | |
if(m > 0.6) { | |
if(runif(1) < 0.8) return("intragenic") | |
} | |
if(m < 0.4) { | |
if(runif(1) < 0.4) return("TSS") | |
} | |
return("intergenic") | |
}) | |
############################################ | |
# distance to genes | |
dist = sapply(mean_meth, function(m) { | |
if(m < 0.6) { | |
if(runif(1) < 0.8) return(round( (runif(1)-0.5)*1000000 + 500000 )) | |
} | |
if(m < 0.3) { | |
if(runif(1) < 0.4) return(round( (runif(1) - 0.5)*1000 + 500)) | |
} | |
return(round( (runif(1) - 0.5)*100000 + 50000)) | |
}) | |
####################################### | |
# annotation to enhancers | |
rand_enhancer = function(m) { | |
if(m < 0.4) { | |
if(runif(1) < 0.6) return(runif(1)) | |
} else if (runif(1) < 0.1) { | |
return(runif(1)) | |
} | |
return(0) | |
} | |
anno_enhancer_1 = sapply(mean_meth, rand_enhancer) | |
anno_enhancer_2 = sapply(mean_meth, rand_enhancer) | |
anno_enhancer_3 = sapply(mean_meth, rand_enhancer) | |
anno_enhancer = data.frame(enhancer_1 = anno_enhancer_1, enhancer_2 = anno_enhancer_2, enhancer_3 = anno_enhancer_3) | |
################################# | |
# put everything into one object | |
res_list = list() | |
res_list$type = type | |
res_list$mat_meth = mat_meth | |
res_list$mat_expr = mat_expr | |
res_list$direction = direction | |
res_list$cor_pvalue = cor_pvalue | |
res_list$gene_type = gene_type | |
res_list$anno_gene = anno_gene | |
res_list$dist = dist | |
res_list$anno_enhancer = anno_enhancer |
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