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HiCzin.R
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#Normalize metagenomic Hi-C data and detect spurious contacts using zero-inflated Negative Binominal regression frameworks
#Auther and maintainer: Yuxuan Du <[email protected]>
#HiCzin R script depends on 'glmmTMB' package
library('glmmTMB')
HiCzin = function(contig_info_file , valid_contact_file , thres)
{
sample_data = read.csv(valid_contact_file , header = F , sep = ',' )
sample_data = as.data.frame(sample_data)
colnames(sample_data) = c('index1' , 'index2' , 'contacts')
contig_info = read.csv(contig_info_file , header = F , sep = ',' )
contig_info = as.data.frame(contig_info)
sample_data[ , 1] = sample_data[ , 1] + 1
sample_data[ , 2] = sample_data[ , 2] + 1
if(ncol(contig_info) == 4){
colnames(contig_info) = c('contig_name' , 'site' , 'length' , 'coverage')
contig_info[contig_info$site==0 , 2] = 1
contig_info[contig_info$coverage==0 , 4] = min(contig_info[contig_info$coverage!=0 , 4])
sample_len = rep(0 , nrow(sample_data))
sample_site = rep(0 , nrow(sample_data))
sample_cov = rep(0 , nrow(sample_data))
for(i in 1:nrow(sample_data))
{
sample_site[i] = log(as.numeric(contig_info[as.numeric(sample_data[i , 1]) , 2]) *
as.numeric(contig_info[as.numeric(sample_data[i , 2]) , 2]))
sample_len[i] = log(as.numeric(contig_info[as.numeric(sample_data[i , 1]) , 3]) *
as.numeric(contig_info[as.numeric(sample_data[i , 2]) , 3]))
sample_cov[i] = log(as.numeric(contig_info[as.numeric(sample_data[i , 1]) , 4]) *
as.numeric(contig_info[as.numeric(sample_data[i , 2]) , 4]))
}
sampleCon = as.numeric(sample_data[ , 3])
mean_site = mean(sample_site)
sd_site = sd(sample_site)
mean_len = mean(sample_len)
sd_len = sd(sample_len)
mean_cov = mean(sample_cov)
sd_cov = sd(sample_cov)
sample_site = (sample_site-mean_site)/sd_site
sample_len = (sample_len-mean_len)/sd_len
sample_cov = (sample_cov-mean_cov)/sd_cov
data_sample = cbind(sample_site , sample_len , sample_cov , sampleCon)
data_sample = as.data.frame(data_sample)
colnames(data_sample) = c('sample_site' , 'sample_len' , 'sample_cov' , 'sampleCon')
tryCatch(
{
fit1 = glmmTMB(sampleCon~sample_site+sample_len+sample_cov, data = data_sample,
ziformula=~sample_site+sample_len+sample_cov , family=nbinom2)
},
error = function(e){
message(e)
message(paste("\nskip", sep=" "))
},
warning = function(w){
message(w)
message(paste("\nskip", sep=" "))
}
)
coeff = as.numeric(fit1$fit$par)
res_sample = sampleCon/exp(coeff[1] + coeff[2]*sample_site + coeff[3]*sample_len+ coeff[4]*sample_cov)
index_nonzero = (res_sample > 0)
res_sample_nonzero = res_sample[index_nonzero]
perc = quantile(res_sample_nonzero , thres)
result = c(coeff[1:4] , perc , mean_site , sd_site , mean_len , sd_len , mean_cov , sd_cov)
return(result)
}else{
colnames(contig_info) = c('contig_name' , 'length' , 'coverage')
sample_len = rep(0 , nrow(sample_data))
sample_cov = rep(0 , nrow(sample_data))
for(i in 1:nrow(sample_data))
{
sample_len[i] = log(as.numeric(contig_info[as.numeric(sample_data[i , 1]) , 2]) *
as.numeric(contig_info[as.numeric(sample_data[i , 2]) , 2]))
sample_cov[i] = log(as.numeric(contig_info[as.numeric(sample_data[i , 1]) , 3]) *
as.numeric(contig_info[as.numeric(sample_data[i , 2]) , 3]))
}
sampleCon = as.numeric(sample_data[ , 3])
mean_len = mean(sample_len)
sd_len = sd(sample_len)
mean_cov = mean(sample_cov)
sd_cov = sd(sample_cov)
sample_len = (sample_len-mean_len)/sd_len
sample_cov = (sample_cov-mean_cov)/sd_cov
data_sample = cbind(sample_len , sample_cov , sampleCon)
data_sample = as.data.frame(data_sample)
colnames(data_sample) = c('sample_len' , 'sample_cov' , 'sampleCon')
tryCatch(
{
fit1 = glmmTMB(sampleCon~sample_len+sample_cov, data = data_sample,
ziformula=~sample_len+sample_cov , family=nbinom2)
},
error = function(e){
message(e)
message(paste("\nskip", sep=" "))
},
warning = function(w){
message(w)
message(paste("\nskip", sep=" "))
}
)
coeff = as.numeric(fit1$fit$par)
res_sample = sampleCon/exp(coeff[1] + coeff[2]*sample_len + coeff[3]*sample_cov)
index_nonzero = (res_sample > 0)
res_sample_nonzero = res_sample[index_nonzero]
perc = quantile(res_sample_nonzero , thres)
result = c(coeff[1:3] , perc , mean_len , sd_len , mean_cov , sd_cov)
return(result)
}
}