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loopscore_functions.r
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loopscore_functions.r
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#loopscore functions
#written by Matt jones, Contact m.jones.18@warwick.ac.uk
filter_gene_list2=function(gene_list){
require(dplyr)
gene_list = gene_list %>% group_by(txStart) %>% filter(n()==1)
gene_list = gene_list %>% group_by(txEnd) %>% filter(n()==1)
#filter to remove chr in the list of chromosomes - just keep numebr
gene_list$chrom=sub('chr', '',gene_list$chrom)
#sort by chromosome and only inlcude 1-22 and x (not m ect)
gene_list2=gene_list[which(gene_list$chrom==1),]
for (i in c(seq(from = 2, to = 22, by = 1),'X')) {
gene_list2 <- rbind(gene_list2, gene_list[which(gene_list$chrom==i),])
}
gene_list<- gene_list2
return(gene_list)
}
filter_gene_list=function(gene_list){
require(dplyr)
gene_list <- gene_list %>% group_by(name2) %>% filter(n()==1)
#filter to remove chr in the list of chromosomes - just keep numebr
gene_list$chrom=sub('chr', '',gene_list$chrom)
#sort by chromosome and only inlcude 1-22 and x (not m ect) gene_list2=gene_list[which(gene_list$chrom==1),]
for (i in c(seq(from = 2, to = 22, by = 1),'X')) {
gene_list2 <- rbind(gene_list2, gene_list[which(gene_list$chrom==i),])
}
gene_list<- gene_list2
return(gene_list)
}
initialize_bedgraph = function(bedgraph) {
chrbins=matrix(c(seq(1:22),'X',rep(NA,46)), nrow=23)
count=0
colnames(chrbins) <- c('chrom','startbin','endbin')
for (i in c(seq(1:22),'X')){
count=count+1
bedgraph_one_chr = bedgraph[which(bedgraph$V1==i),]
first = min(bedgraph_one_chr$V4)
last = max(bedgraph_one_chr$V4)
chrbins[count,2]=bedgraph$V4[first]
chrbins[count,3]=bedgraph$V4[last]
}
return(chrbins)
}
calculate_average_interactions = function(chia_pet_matrix,chrbins,resolution) {
averageinteractions_all_chrom=c(NA, NA,NA)
for( chromosome in c(seq(1:22),'X')) {
startbin= as.numeric(chrbins[which(chrbins[,1]==chromosome),2])
endbin= as.numeric(chrbins[which(chrbins[,1]==chromosome),3])
chr_pet <- chia_pet_matrix[which(chia_pet_matrix$V1 < endbin),]
chr_pet <- chr_pet[which(chr_pet$V1> startbin),]
chr_pet <- chr_pet[which(chr_pet$V2<endbin),]
chr_pet <- chr_pet[which(chr_pet$V2> startbin),]
genomic_distance <- (chr_pet$V2-chr_pet$V1)*resolution
chr_distance <- cbind(chr_pet, genomic_distance)
number_of_bins = (as.numeric(endbin)-as.numeric(startbin))
distances = seq(from=0, to=number_of_bins*resolution, by = resolution)
averageinteractions=c()
for (i in 1:2100) {
interactions=c()
interactions = chr_distance[['V3']][which(chr_distance[['genomic_distance']]==distances[i])]
number_of_zeros = number_of_bins-(length(interactions)+i) #the i is important, draw a matrix to realize
interactions = c(interactions, rep(0, number_of_zeros))
meaninteractions=mean(interactions)
averageinteractions=c(averageinteractions, meaninteractions)
}
averageinteractions=cbind(rep(chromosome, length(averageinteractions)), averageinteractions, distances[1:2100])
averageinteractions_all_chrom <- rbind(averageinteractions_all_chrom, averageinteractions )
}
averageinteractions_all_chrom=na.omit(averageinteractions_all_chrom)
return(averageinteractions_all_chrom)}
calculate_loopscores = function (chia_pet_matrix, chrbins, gene_list) {
loopscoreslist=matrix(c(NA,NA,NA,NA,NA),nrow=1)
colnames(loopscoreslist) <- c('gene_name','chrom','txStart','txEnd','score')
for (gene in 1:nrow(gene_list)){
gene_name = as.character(gene_list$name2[gene])
chrom=gene_list$chrom[gene]
startbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),2]) + floor(gene_list$txStart[gene]/resolution) #note floor here, because of the start bin
endbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),2]) + floor(gene_list$txEnd[gene]/resolution) #note floor here, because of the start bin
instart = chia_pet_matrix[which(chia_pet_matrix$V1==startbin),]
genescore=instart$V3[which(instart$V2==endbin)]
if(length(genescore) > 0) {
loopscoreslist=rbind(loopscoreslist,c(gene_name,chrom,as.numeric(gene_list$txStart[gene]),as.numeric(gene_list$txEnd[gene]),genescore))
}
if (length(genescore)==0) {
genescore = 0
loopscoreslist=rbind(loopscoreslist,c(gene_name,chrom,as.numeric(gene_list$txStart[gene]),as.numeric(gene_list$txEnd[gene]),genescore))
}
}
return(loopscoreslist)}
size_normalise_loopscore = function(loopscoreslist, averageinteractions_all_chrom) {
loopscoreslist <- na.omit(loopscoreslist)
adjusted_loopscore=c()
#now to calculate the loopscore size normalized
for( chromosome in c(seq(1:22),'X')) {
averageinteractions <- averageinteractions_all_chrom[which(averageinteractions_all_chrom[,1] == chromosome),2]
chr_loopscore_list = loopscoreslist[which(loopscoreslist[['chrom']]==chromosome),]
startbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chromosome),2]) + floor(as.numeric(chr_loopscore_list$txStart)/resolution) #note floor here, because of the start bin
endbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chromosome),2]) + floor(as.numeric(chr_loopscore_list$txEnd)/resolution) #
binsize = endbin-startbin
expectedloopscores = averageinteractions[binsize+1] #note the plus 1 is because if binsize = 0 we want the first row of average interactions
chr_loopscore_list$score = as.numeric(chr_loopscore_list$score)/as.numeric(expectedloopscores)
chr_loopscore_list <- cbind(chr_loopscore_list, expectedloopscores)
adjusted_loopscore = rbind(adjusted_loopscore,chr_loopscore_list)
}
return(adjusted_loopscore)
}
compare_loop_to_noise = function(adjusted_loopscore, readcounts) {
loopscoreslist<- adjusted_loopscore
loopscoreslist=na.omit(loopscoreslist)
noise_compared_loop=na.omit(loopscoreslist)
means = rep(NA, nrow(noise_compared_loop))
cv = rep(NA, nrow(noise_compared_loop))
burst_freq = rep(NA, nrow(noise_compared_loop))
burst_size = rep(NA, nrow(noise_compared_loop))
noise_compared_loop = cbind(noise_compared_loop, means)
noise_compared_loop = cbind(noise_compared_loop, cv)
noise_compared_loop = cbind(noise_compared_loop, burst_freq)
noise_compared_loop = cbind(noise_compared_loop, burst_size)
for (i in 1:nrow(loopscoreslist)) {
gene_name=as.character(loopscoreslist[i,1])
whatgene = which(readcounts[,1]==gene_name)
if (any(whatgene)==T) {
variance = var(as.numeric(na.omit(as.numeric(readcounts[whatgene,2:ncol(readcounts)]))))
meanreads=mean(as.numeric(na.omit(as.numeric(readcounts[whatgene,2:ncol(readcounts)]))))
if (is.na(meanreads)==F){
if (meanreads==0) {CVs=NA}
if (abs(meanreads)>0) { CVs=sqrt(variance)/meanreads }
noise_compared_loop[i,7]=meanreads
noise_compared_loop[i,8]=CVs
noise_compared_loop[i,9]=meanreads^2/(variance-meanreads)
noise_compared_loop[i,10]=(variance-meanreads)/meanreads}
if (any(whatgene)==F) {
noise_compared_loop[i,7]=NA
noise_compared_loop[i,8]=NA
noise_compared_loop[i,9]=NA
noise_compared_loop[i,10]=NA}
#print(i/nrow(chr_loopscores))
}}
res <- na.omit(noise_compared_loop )
return(res)
}
#new function (massimo's choice) to size adjust the loopscore have not done this yet!
#davids idea = get v3 as vector
#make the rownames = paste the two numbers together we are looking for -> go straight to that
calculate_and_adjust_loopscores = function (chia_pet_matrix, chrbins, gene_list,no_genes, resolution) {
loopscoreslist=matrix(c(NA,NA,NA,NA,NA),nrow=1)
colnames(loopscoreslist) <- c('gene_name','chrom','txStart','txEnd','score')
list_of_interactions = chia_pet_matrix$V3
my_rownames = paste(chia_pet_matrix$V1, chia_pet_matrix$V2, sep='_')
rownames(chia_pet_matrix) <- my_rownames
for (gene in 1:100) { #nrow(gene_list)){
gene_name = as.character(gene_list$name2[gene])
chrom=gene_list$chrom[gene]
gene_size = gene_list$txEnd[gene]-gene_list$txStart[gene]
startbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),2]) + floor(gene_list$txStart[gene]/resolution) #note floor here, because of the start bin
endbin = as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),2]) + floor(gene_list$txEnd[gene]/resolution) #note floor here, because of the start bin
loopscore_row_name=paste(as.character(startbin), '_', as.character(endbin), sep='')
genescore = chia_pet_matrix[loopscore_row_name,3]
if(is.na(genescore)==F) {
random_start_locations = sample(seq(from=as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),2]),to=as.numeric(chrbins[which(as.character(chrbins[,1])==chrom),3]) - ceiling(gene_size/resolution)),no_genes, replace =T)
random_end_locations = random_start_locations + ceiling(gene_size/resolution)
random_loopscore_rownames = paste(as.character(random_start_locations), '_', as.character(random_end_locations), sep='')
random_scores = chia_pet_matrix[random_loopscore_rownames,3]
random_scores[is.na(random_scores)] <- 0
adjusted_genescore=genescore/mean(random_scores)
loopscoreslist=rbind(loopscoreslist,c(gene_name,chrom,as.numeric(gene_list$txStart[gene]),as.numeric(gene_list$txEnd[gene]),adjusted_genescore))
}
if(is.na(genescore)==T) {
adjusted_genescore=0
loopscoreslist=rbind(loopscoreslist,c(gene_name,chrom,as.numeric(gene_list$txStart[gene]),as.numeric(gene_list$txEnd[gene]),adjusted_genescore))
}}
return(loopscoreslist)
}