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Identify_HRPI.R
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Identify_HRPI.R
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sumres_v2=function()
{
require(data.table)
# require(ggplot2)
# require(yaml)
require(officer)
require(flextable)
require(dplyr)
require(doParallel)
arguments = commandArgs(trailingOnly = TRUE)
inputDir = as.character(arguments[1])
# outputDir = as.character(arguments[2])
metadataFile = as.character(arguments[2])
# coff=as.numeric(as.character(arguments[3])) # for relatadeness
meta = fread(metadataFile,header=TRUE)
# allFiles = list.files(path = outputDir, pattern = '*.RDS', full.names = TRUE)
cutoffs = c(0.2, 0.3, 0.4, 0.5, 0.6)
for(inc in 1:length(cutoffs)){
coff = cutoffs[inc]
outputDir = paste0(inputDir,"/HRIP_",coff) #HRIP stands for highly related infection pairs
system(sprintf("mkdir -p %s", outputDir))
allFiles = list.files(path = inputDir, pattern = '*_all_Relatedness.RDS', full.names = TRUE)
cl = makeCluster(5)
registerDoParallel(cl)
foreach(i = 1:length(allFiles), .packages = c("dplyr","flextable","officer","data.table")) %dopar%{
site1 = unlist(strsplit(basename(allFiles[i]),'_'))[1]
site2 = unlist(strsplit(basename(allFiles[i]),'_'))[2]
prefix = paste0(site1,'_',site2)
target = allFiles[i]
Xgam=readRDS(target) #Xgam=readRDS(f)
modifier = function(x){gsub('-','.',x)}
meta$SampleID=lapply(meta$SampleID,modifier)
m = match(Xgam$iid1, meta$SampleID)
Xgam$site1=meta$Location[m]
Xgam$year1=meta$Year[m]
m = match(Xgam$iid2, meta$SampleID)
Xgam$site2=meta$Location[m]
Xgam$year2=meta$Year[m]
g_gam=Xgam[,.(count = .N),by=list(site1,site2,year1,year2)]
nams_gam=c(paste0(g_gam$site1,g_gam$year1),paste0(g_gam$site2,g_gam$year2))
g_gam=as.matrix(g_gam)
if(nrow(g_gam)==1)
{
Lgam=list()
for ( j in 1 : (nrow(g_gam)+1)){
if(j==1)
Lgam[[j]]=Xgam[Xgam$site1==g_gam[1,j] & Xgam$year1==as.numeric(g_gam[1,(j+2)]),]$iid1
else
Lgam[[j]]=Xgam[Xgam$site2==g_gam[1,j] & Xgam$year2==as.numeric(g_gam[1,(j+2)]),]$iid2
}
relatedPairs=list()
allPairs=list()
ff=1
nams_freq = c('maf>0','maf>10','maf>20','maf>30','maf>40')
outlistName=NULL
X = matrix(NA, 1, 6) #X = matrix(NA, 1, 7)
colnames(X)=c('sitePairs','maf>0%','maf>10%','maf>20%','maf>30%','maf>40%') #colnames(X)=c('sitePairs','maf>0%','maf>10%','maf>20%','maf>30%','maf>40%', '#Pairs')
for (f in 1:5)
{
targetRelatedness = as.matrix(subset(Xgam, select = (f+2)))
targetRelatedness=as.numeric(targetRelatedness[,1])
Yout_gam = numeric(1)
dum=character(1)
dum[1]=paste0(nams_gam[1],"_vs_",nams_gam[2])
r = match(Xgam$iid1,unlist(Lgam[[1]]))
j = match(Xgam$iid2,unlist(Lgam[[2]]))
k = !is.na(r) & !is.na(j)
Xgam_prime = Xgam[k,]
mm=which(targetRelatedness[k]>coff)
Yout_gam[1]=100*length(mm)/sum(k)
allPairs[[ff]] = Xgam_prime
relatedPairs[[ff]] = subset(Xgam_prime[mm,], select = c(1:2,(f+2),8:ncol(Xgam_prime)))
outlistName = c(outlistName, paste0(dum[1],'_',nams_freq[f]))
ff=ff+1
X[1,(f+1)]=round(Yout_gam,2)
# X[1,7]=length(mm)
if (f==1){
X[1,f]=dum
}
}
names(relatedPairs) = outlistName
names(allPairs) = outlistName
}
else
{
Lgam = list()
comb1 = paste0(g_gam[,3],g_gam[,4])
comb2 = paste0(g_gam[,4],g_gam[,3])
sameComb = data.frame(index1=numeric(),index2=numeric())
ooo=1
saved=NULL
for(o in 1:length(comb1))
{
if(o %in% saved)
next
for(oo in 1:length(comb2))
{
if(comb1[o]==comb2[oo] & o!=oo)
{
sameComb[ooo,1]=o
sameComb[ooo,2]=oo
ooo=ooo+1
saved = c(saved, oo)
}
}
}
if(nrow(sameComb)==0)
{
n=1
for ( j in 1 : nrow(g_gam)){
popTarget = Xgam[which((Xgam$site1==g_gam[j,1] & Xgam$year1==g_gam[j,3]) & (Xgam$site2==g_gam[j,2] & Xgam$year2==g_gam[j,4])),]
Lgam[[n]]=popTarget[popTarget$site1==g_gam[j,1] & popTarget$year1==as.numeric(g_gam[j,3]),]$iid1
n=n+1
Lgam[[n]]=popTarget[popTarget$site2==g_gam[j,2] & popTarget$year2==as.numeric(g_gam[j,4]),]$iid2
n=n+1
}
}
else
{
saved=NULL
n=1
for ( j in 1 : nrow(g_gam)) #{ #(2*length(unique(g_gam[,3])))
{
if(j %in% saved)
next
else
{
v1 =match(j,sameComb$index1) #which(!is.na(match(j,sameComb$index1)))
if(length(v1)!=0)
{
saved = c(saved, sameComb$index2[v1])
popTarget1 = Xgam[which((Xgam$site1==g_gam[j,1] & Xgam$year1==g_gam[j,3]) & (Xgam$site2==g_gam[j,2] & Xgam$year2==g_gam[j,4])),]
popTarget2 = Xgam[which((Xgam$site1==g_gam[sameComb$index2[v1],1] & Xgam$year1==g_gam[sameComb$index2[v1],3] ) & (Xgam$site2==g_gam[sameComb$index2[v1],2] & Xgam$year2==g_gam[sameComb$index2[v1],4])),]
popTarget = rbind(popTarget1, popTarget2)
lgam1=popTarget1[popTarget1$site1==g_gam[j,1] & popTarget1$year1==as.numeric(g_gam[j,3]),]$iid1
lgam2=popTarget2[popTarget2$site1==g_gam[sameComb$index2[v1],1] & popTarget2$year1==as.numeric(g_gam[sameComb$index2[v1],3]),]$iid1
Lgam[[n]]=c(lgam1, lgam2)#popTarget[popTarget$site1==g_gam[j,1] & popTarget$year1==as.numeric(g_gam[j,3]),]$iid1
n=n+1
lgam1=popTarget1[popTarget1$site2==g_gam[j,2] & popTarget1$year2==as.numeric(g_gam[j,4]),]$iid2
lgam2=popTarget2[popTarget2$site2==g_gam[sameComb$index2[v1],2] & popTarget2$year2==as.numeric(g_gam[sameComb$index2[v1],4]),]$iid2
Lgam[[n]]=c(lgam1, lgam2)#popTarget[popTarget$site2==g_gam[j,2] & popTarget$year2==as.numeric(g_gam[j,4]),]$iid2
n=n+1
}
else
{
popTarget = Xgam[which((Xgam$site1==g_gam[j,1] & Xgam$year1==g_gam[j,3]) & (Xgam$site2==g_gam[j,2] & Xgam$year2==g_gam[j,4])),]
Lgam[[n]]=popTarget[popTarget$site1==g_gam[j,1] & popTarget$year1==as.numeric(g_gam[j,3]),]$iid1
n=n+1
Lgam[[n]]=popTarget[popTarget$site2==g_gam[j,2] & popTarget$year2==as.numeric(g_gam[j,4]),]$iid2
n=n+1
}
}
}
}
relatedPairs=list()
allPairs=list()
ff=1
nams_freq = c('maf>0','maf>10','maf>20','maf>30','maf>40')
outlistName=NULL
X = matrix(NA, (nrow(g_gam)-nrow(sameComb)), 6) #X = matrix(-9, length(nams_gam_unique), 7)
colnames(X)=c('sitePairs','maf>0%','maf>10%','maf>20%','maf>30%','maf>40%') #colnames(X)=c('sitePairs','maf>0%','maf>10%','maf>20%','maf>30%','maf>40%', '#Pairs')
# u_year2 = length(unique(g_gam[,4]))
for (f in 1:5)
{
targetRelatedness = as.matrix(subset(Xgam, select = (f+2)))
targetRelatedness=as.numeric(targetRelatedness[,1])
Yout_gam = numeric((nrow(g_gam)-nrow(sameComb))) #numeric(length(nams_gam_unique))
dum=character((nrow(g_gam)-nrow(sameComb))) #character(length(nams_gam_unique))
numPairs = numeric((nrow(g_gam)-nrow(sameComb)))
county = 0; m=1; q=1
while(m<=length(Lgam)) #while(m<=(length(Lgam)-u_year2)) #for ( m in 1 : nrow(g_gam))
{
if(!is.null(saved) & q %in% saved)
q=q+1
n=m+1 #n=(length(Lgam)-u_year2)+1
county = county + 1
dum[county]=paste0(g_gam[q,1],g_gam[q,3],"_vs_",g_gam[q,2],g_gam[q,4]) #paste0(nams_gam_unique[m],"_vs_",nams_gam_unique[n])
r = match(Xgam$iid1,unlist(Lgam[[m]]))
j = match(Xgam$iid2,unlist(Lgam[[n]]))
k = !is.na(r) & !is.na(j)
Xgam_prime = Xgam[k,]
mm=which(targetRelatedness[k]>coff)
Yout_gam[county]=100*length(mm)/sum(k)
numPairs[county] = length(mm)
allPairs[[ff]] = Xgam_prime
relatedPairs[[ff]] = subset(Xgam_prime[mm,], select = c(1:2,(f+2),8:ncol(Xgam_prime)))
outlistName = c(outlistName, paste0(dum[county],'_',nams_freq[f]))
ff=ff+1; m=m+2; q=q+1
}
X[1:length(Yout_gam),(f+1)]=as.numeric(round(Yout_gam,2))
# X[1:length(Yout_gam),7]=numPairs
if (f==1){
X[1:length(Yout_gam),f]=dum
}
}
names(relatedPairs) = outlistName
names(allPairs) = outlistName
}
saveRDS(X, paste0(outputDir,'/',prefix,"_Percentage_HRI.RDS"))
ft=flextable(as.data.frame(X)) %>% autofit()
colorToUse = c('#EFEFEF','yellow','cyan','steelblue','orange','purple','lightblue','lightgreen','pink','navy')
for(o in 1:nrow(X)){
ft = bg(ft, i = o, bg = colorToUse[o], part = "body")}
ft = add_footer_row(ft, values = paste0(prefix," relatedness cut-off = ",coff), colwidths = 6, top = FALSE)
#ft = merge_at(ft, j = 1:5, part = "footer")# theme the tableft <- theme_box(ft)
save_as_html(ft,path=paste0(outputDir,'/',prefix,"_MAFp2.html"),title="MAF")
saveRDS(relatedPairs, paste0(outputDir,'/',prefix,"_HRI.RDS"))
saveRDS(allPairs, paste0(outputDir,'/',prefix,"_All_Pairs.RDS"))
}
stopCluster(cl)
}
}
sumres_v2()