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Copy pathCompare_WithinGenome_Population_Pi.R
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Compare_WithinGenome_Population_Pi.R
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# identify possible IBD regions within genomes
# compare heterozygosity in a region to median pi in background
#for autosomes
#1800s: H10, H11, H13, H9, H25
#1933: H1, H8, H14-24
setwd("/raid10/mshpak/MuseumFlies/IBD_Calculations")
chrom = "Chr3L" #change as needed
outfile_name = paste("Full_Mat_", chrom, sep = '')
outfile_name = paste(outfile_name, ".csv", sep = '')
Full_Mat = read.csv(outfile_name, header=TRUE)
Full_Mat = Full_Mat[,1:24]
#chr_window_filename = paste("windows_ZI10000_", chrom, sep = '')
chr_window_filename = paste("windows_ZI25000_", chrom, sep = '')
chr_window_filename = paste(chr_window_filename, ".txt", sep = '')
window_intervals = read.csv(chr_window_filename, sep = '\t', header=FALSE)
#3L
elements_1800 = c(3,1,4,6,7,5)
pairs_1800 = c()
for(i in 1:(length(elements_1800)-1)){
for(j in (i+1):length(elements_1800)){
pairs_1800 = rbind(pairs_1800, c(elements_1800[i],elements_1800[j]))
}
}
# 1933 priority masking order H3, 7, 1, 16, 15, 21, 8, 22, 14, 17, 18, 19, 20, 24, 23
elements_1933 = c(10, 11, 12, 16, 15, 21, 13, 22, 14, 17, 18, 19, 20, 24, 23)
pairs_1933 = c()
for(i in 1:(length(elements_1933)-1)){
for(j in (i+1):length(elements_1933)){
pairs_1933 = rbind(pairs_1933, c(elements_1933[i], elements_1933[j]))
}
}
assoc_exclude_1800 = list()
assoc_exclude_1800[[1]] = c(1,3,4)
assoc_exclude_1800[[3]] = c(3,1,4,5,7)
assoc_exclude_1800[[4]] = c(4,1,3,6)
assoc_exclude_1800[[5]] = c(5,3,4)
assoc_exclude_1800[[6]] = c(6,4)
assoc_exclude_1800[[7]] = c(7,3)
assoc_exclude_1800[[8]] = c(8,9)
assoc_exclude_1800[[9]] = c(9,8)
assoc_exclude_1933 = list()
assoc_exclude_1933[[10]] = c(10,12,19)
assoc_exclude_1933[[11]] = c(11,24)
assoc_exclude_1933[[12]] = c(12,10,13,14)
assoc_exclude_1933[[13]] = c(13,14)
assoc_exclude_1933[[14]] = c(14,13,22)
assoc_exclude_1933[[15]] = c(15,23)
assoc_exclude_1933[[16]] = c(16,23)
assoc_exclude_1933[[17]] = c(17)
assoc_exclude_1933[[18]] = c(18,19)
assoc_exclude_1933[[19]] = c(19,10,18)
assoc_exclude_1933[[20]] = c(20)
assoc_exclude_1933[[21]] = c(21)
assoc_exclude_1933[[22]] = c(22,13)
assoc_exclude_1933[[23]] = c(23,15,16)
assoc_exclude_1933[[24]] = c(24,11)
seq_ham_dist = function(v1,v2){
return(sum(v1 != v2, na.rm = TRUE))
}
# average distance in relation to non-na sites among pairs
pairwise_pi = function(v1,v2){
nsites = which(is.na(v1)==FALSE & is.na(v2)==FALSE)
sdist = seq_ham_dist(v1,v2)
return(sdist/length(nsites))
}
comp_mat_distances = function(M1){
Dmat = c()
for(i in 1:length(M1[1,])){
vtemp = c()
for(j in 1:length(M1[1,])){
dist = pairwise_pi(M1[,i],M1[,j])
vtemp = c(vtemp, dist)
}
Dmat = rbind(Dmat, vtemp)
}
return(Dmat)
}
comp_mat_distances = function(M1,samples){
Dmat = c()
for(i in 1:length(samples)){
vtemp = c()
for(j in 1:length(M1[1,])){
dist = pairwise_pi(M1[,i],M1[,j])
vtemp = c(vtemp, dist)
}
Dmat = rbind(Dmat, vtemp)
}
return(Dmat)
}
Hap1800 = c('H4','H5','H6','H12')
Hap1933 = c('H3','H7')
Dip1800 = c('H10','H11','H13','H9','H25')
Dip1933 = c('H1','H8','H14','H15','H16','H17','H18','H19','H20','H21','H22','H23','H24')
museum_samples = c(Hap1800,Dip1800,Hap1933,Dip1933)
all_1800 = unique(elements_1800)
all_1933 = unique(elements_1933)
#window_correction_factors = c() #per window per sample
window_pi_1800 = c() # per window across all samples
window_pi_1933 = c()
for(i in 1:length(window_intervals[,1])){
start = window_intervals[i,1]+1
end = window_intervals[i,2]+1
part_mat = Full_Mat[start:end,]
Dist_Part_Mat = comp_mat_distances(part_mat,museum_samples)
#correct_factors = rep(1,length(museum_samples))
avg_win_dist_1800 = c()
for(j in all_1800){
vtemp = Dist_Part_Mat[j, all_1800]
vtemp = vtemp[-assoc_exclude_1800[[j]]]
med_dist = median(vtemp, na.rm = TRUE)
#correct_factors[j] = med_dist
avg_win_dist_1800 = c(avg_win_dist_1800, med_dist)
}
pop_avg_1800 = median(avg_win_dist_1800, na.rm = TRUE)
window_pi_1800 = c(window_pi_1800, pop_avg_1800)
#correct_factors[all_1800] = correct_factors[all_1800]/pop_avg_1800
avg_win_dist_1933 = c()
for(j in all_1933){
vtemp = Dist_Part_Mat[j, all_1933]
vtemp = vtemp[-assoc_exclude_1933[[j]]]
med_dist = median(vtemp, na.rm = TRUE)
#correct_factors[j] = med_dist
avg_win_dist_1933 = c(avg_win_dist_1933, med_dist)
}
pop_avg_1933 = median(avg_win_dist_1933, na.rm = TRUE)
window_pi_1933 = c(window_pi_1933, pop_avg_1933)
#correct_factors[all_1933] = correct_factors[all_1933]/pop_avg_1933
#window_correction_factors = rbind(window_correction_factors, correct_factors)
}
#number of distinct nucleotides (0,1 or 2)
nuc_count = function(v){
return(length(which(v>0)))
}
heterozygosity = function(M1){
vsum1 = rowSums(M1)
to_keep = which(vsum1 > 0)
mat_temp = M1[to_keep,]
allele_counts = apply(mat_temp, 1, nuc_count)
het = length(which(allele_counts > 1))/length(to_keep)
return(het)
}
setwd("/raid10/mshpak/MuseumFlies/IndivCounts/MuseumIndivs")
#Count_File = read.csv(indiv_count_name, sep='\t', header=FALSE)
dict_1800 = list()
dict_1933 = list()
samp_1933 = c("H1","H8","H14","H15","H16","H17","H18","H19","H20","H21","H22","H23","H24")
samp_1800 = c("H10","H11","H13","H9","H25")
#samp_1933 = c("H1","H8")
for(samp in samp_1933){
indiv_count_name = paste("IndivCounts_", samp, sep='')
indiv_count_name = paste(indiv_count_name, "_NoInv_", sep='')
indiv_count_name = paste(indiv_count_name, chrom, sep='')
indiv_count_name = paste(indiv_count_name, ".txt", sep='')
Count_File = read.csv(indiv_count_name, sep='\t', header=FALSE)
het_windows = c()
to_mask = c()
for(i in 1:length(window_intervals[,1])){
start = window_intervals[i,1]+1
end = window_intervals[i,2]+1
Part_Mat = Count_File[start:end,]
het_temp = heterozygosity(Part_Mat)
het_windows = c(het_windows, het_temp)
if(het_temp < window_pi_1933/2){
to_mask = c(to_mask, i)
}
}
dict_1933[[samp]] = to_mask
}
for(samp in samp_1800){
indiv_count_name = paste("IndivCounts_", samp, sep='')
indiv_count_name = paste(indiv_count_name, "_NoInv_", sep='')
indiv_count_name = paste(indiv_count_name, chrom, sep='')
indiv_count_name = paste(indiv_count_name, ".txt", sep='')
Count_File = read.csv(indiv_count_name, sep='\t', header=FALSE)
het_windows = c()
to_mask = c()
for(i in 1:length(window_intervals[,1])){
start = window_intervals[i,1]+1
end = window_intervals[i,2]+1
Part_Mat = Count_File[start:end,]
het_temp = heterozygosity(Part_Mat)
het_windows = c(het_windows, het_temp)
if(het_temp < window_pi_1800/2){
to_mask = c(to_mask, i)
}
}
dict_1800[[samp]] = to_mask
}
#
max_len = length(window_intervals[,1])
To_Remove_1800 = c()
for(samp in names(dict_1800)){
tempv = c(samp, dict_1800[[samp]])
length(tempv)=max_len
To_Remove_1800 = rbind(To_Remove_1800, tempv)
}
To_Remove_1933 = c()
for(samp in names(dict_1933)){
tempv = c(samp, dict_1933[[samp]])
length(tempv)=max_len
To_Remove_1933 = rbind(To_Remove_1933, tempv)
}
To_Remove = rbind(To_Remove_1800, To_Remove_1933)
# change this to appropriate chromosome
write.csv(To_Remove, file="WithinGenome_Remove_Windows_3L.csv", row.names=FALSE)
#