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post_prediction_tools.R
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# FUNCTIONS
point_score = function(locus,radius=500000,M, pseudocount=0.1){
l_edge = max((locus - radius),1)
r_edge = min((locus + radius),nrow(M))
l_mask = M[,l_edge:locus]
r_mask = M[,locus:r_edge]
center_mask = M[l_edge:locus,locus:r_edge]
score = (max(mean(l_mask,na.rm=T),mean(r_mask,na.rm=T)) + pseudocount) / (mean(center_mask) + pseudocount)
return(score)
}
region_score = function(M,resolution=8192,radius=500000 ,pseudocount=0.1){
M = as.matrix(M)
pixel_radius = ceiling(radius/resolution)
scores = unlist(lapply(1:nrow(M),point_score,radius=pixel_radius,M=M,pseudocount=pseudocount))
return(scores)
}
save_scores = function(prediction_file,inpdir,window=2097152,resolution=8192,radius=500000){
# Data reading
coord = gsub(x=prediction_file,pattern = ".npy",replacement = "")
chr_start=unlist(strsplit(coord,"_"))
outfile= paste0("contact_maps_",coord,".pdf")
m_pred <- as.data.frame(np$load(file.path(inpdir,prediction_file)))
rscl = abs(min(m_pred))
m_pred = m_pred+rscl
# HiC values
preds = m_pred[upper.tri(m_pred, diag = F)]
# Insulation values
prediction_scores = region_score(M=m_pred,resolution=resolution,radius=radius,pseudocount=pseudocount)
# Bin coordinates
n_bins=nrow(m_pred)
resolution=window/(n_bins)
chr=chr_start[1]
start=as.numeric(chr_start[2])
end=start+window-1
start_seq = seq(start,end,resolution)
end_seq = seq(start_seq[2]-1,end,resolution)
return(list(chr=rep(chr,n_bins),start=start_seq,end=end_seq,
pred_scores=prediction_scores,window=rep(coord,n_bins)))
}
make_scores_bed = function(results,inpdir,outname="prediction_scores.csv"){
chr=vector(mode = "character")
start=vector(mode = "numeric")
end=vector(mode = "numeric")
scores=vector(mode = "numeric")
window=vector(mode = "character")
for(i in 1:length(results)){
xi=results[[i]]
for(j in 1:length(xi)){
xij=unlist(xi[j])
if(j==1){ chr = c(chr,xij) }
if(j==2){ start = c(start,xij) }
if(j==3){ end = c(end,xij) }
if(j==4){ scores = c(scores,xij) }
if(j==5){ window = c(window,xij) }
}
}
scores_bed = data.frame(chr=chr,start=start,end=end,score=scores,window=window,stringsAsFactors = F)
write.csv(scores_bed,file.path(inpdir,outname),row.names = F)
}
compute_scores_parallel = function(sample_name,save_target=F,n_cores=6){
print(sample_name)
inpdir = file.path(wd,sample_name,"prediction/npy/")
matrix_files = list.files(inpdir,pattern = ".npy")
target_files = matrix_files[grep("_target.npy",matrix_files,invert = F)]
prediction_files = matrix_files[grep("_target.npy",matrix_files,invert = T)]
if(length(prediction_files) != n_windows){ print(paste0("WARNING: ",sample_name)," does not have ",n_windows," matrices.") }
print("Prediction scores...")
results = mclapply(prediction_files,save_scores,inpdir=inpdir,mc.cores = n_cores)
make_scores_bed(results,inpdir=inpdir)
if(save_target){
print("Target scores...")
results = mclapply(target_files,save_scores,inpdir=inpdir,mc.cores = n_cores)
make_scores_bed(results,inpdir=inpdir,outname="target_scores.csv")
}
}
save_hic = function(matrix_file,inpdir,window=2097152,resolution=8192,radius=500000){
# Data reading
coord = gsub(x=matrix_file,pattern = "_target.npy",replacement = "")
coord = gsub(x=coord,pattern = ".npy",replacement = "")
chr_start=unlist(strsplit(coord,"_"))
outfile= paste0("contact_maps_",coord,".pdf")
m <- as.data.frame(np$load(file.path(inpdir,matrix_file)))
rscl = abs(min(m))
m = m+rscl
# Bin coordinates
n_bins=nrow(m)
resolution=window/(n_bins)
chr=chr_start[1]
start=as.numeric(chr_start[2])
end=start+window-1
start_seq = seq(start,end,resolution)
# Create matrix with coordinate index
m_idx=as.data.frame(matrix(nrow=n_bins,ncol=n_bins))
for (i in 1:n_bins){
for (j in 1:n_bins){
m_idx[i,j]=paste0(start_seq[i],":",start_seq[j])
}
}
# Get HiC values and coords
hic = m[upper.tri(m, diag = F)]
coords = m_idx[upper.tri(m_idx, diag = F)]
return(list(pred=hic,coord=coords))
}
make_hic_bed = function(results,inpdir,chr,outname="prediction_hic"){
hic=vector(mode = "numeric")
coords=vector(mode = "character")
for(i in 1:length(results)){
xi=results[[i]]
for(j in 1:length(xi)){
xij=unlist(xi[j])
if(j==1){ hic = c(hic,xij) }
if(j==2){ coords = c(coords,xij) }
}
}
hic_bed = data.frame(coords=coords,hic=hic,stringsAsFactors = F)
write.csv(hic_bed,file.path(inpdir,paste0(outname,"_",chr,".csv")),row.names = F)
}
extract_hic_parallel = function(sample_name,save_target=F,n_cores=6){
print(sample_name)
inpdir = file.path(wd,sample_name,"prediction/npy/")
matrix_files = list.files(inpdir,pattern = ".npy")
target_files = matrix_files[grep("_target.npy",matrix_files,invert = F)]
prediction_files = matrix_files[grep("_target.npy",matrix_files,invert = T)]
if(length(prediction_files) != n_windows){ print(paste0("WARNING: ",sample_name," does not have ",n_windows," matrices.")) }
chromosomes = unique(gsub(pattern = "_.*.npy",replacement = "",x = prediction_files))
for (chr in chromosomes){
print(chr)
prediction_files_chr = prediction_files[grep(paste0("^",chr,"_"),prediction_files,fixed = F)]
target_files_chr = target_files[grep(paste0("^",chr,"_"),target_files,fixed = F)]
print("Prediction HiC...")
results = mclapply(prediction_files_chr,save_hic,inpdir=inpdir,window=window,mc.cores = n_cores)
make_hic_bed(results,inpdir=inpdir,chr=chr)
if(save_target){
print("Target HiC...")
results = mclapply(target_files_chr,save_hic,inpdir=inpdir,window=window,mc.cores = n_cores)
make_hic_bed(results,inpdir=inpdir,chr=chr,outname="target_hic")
}
}
}
main = function(n_cores=6,extract_hic=T,compute_scores=T,save_target=F){
if(extract_hic) {
print("Extracting HiC data...")
lapply(sample_names,extract_hic_parallel,save_target=save_target,n_cores=n_cores) }
if(compute_scores) {
print("Computing insulation scores...")
lapply(sample_names,compute_scores_parallel,save_target=save_target,n_cores=n_cores) }
}
### RUN #########################
library(reticulate)
library(parallel)
np <- import("numpy")
wd=getwd()
window=2097152
n_windows=1359 # genome_size(chr1:chr22 + chrX) / window
resolution=8192 # 2097152 / 256
radius=500000 # insulation param
pseudocount=0.1
n_cores=8
extract_hic=T
compute_scores=T
save_target=T
sample_names = c("DNO3")
main(n_cores=n_cores,extract_hic=extract_hic,compute_scores=compute_scores,save_target=save_target)