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GbyE.function.R
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GbyE.function.R
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GbyE.file.Calculate <- function(GD,GM,file.output){
if(!require(data.table)) install.packages("data.table")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~GbyE.GM的处理
GM.L1=GM
GM.L2=GM
GM.L2[,2]=GM.L2[,2]+max(GM.L2[,2])
GM.L1[,1]=paste(GM.L1[,1],"-1",sep="")
GM.L2[,1]=paste(GM.L2[,1],"-2",sep="")
GbyE.GM=rbind(GM.L1,GM.L2)
#rm(GM.L1)
#rm(GM.L2)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~GbyE.GD的处理
GD.d=apply(GD[,-1],2,as.numeric)
Matrix.r=matrix(1,2,2)
Matrix.r[1,2]=0
GbyE.GD=kronecker(Matrix.r,as.matrix(GD.d))
#rm(GD.d)
#rm(Matrix.r)
Taxa="taxa"
#row.name=rbind(as.data.frame(c(as.character(Taxa),paste(GD[,1],"-1",sep=""),paste(GD[,1],"-2",sep=""))))
row.name=rbind(as.data.frame(c(paste(GD[,1],"-1",sep=""),paste(GD[,1],"-2",sep=""))))
#row.name=rbind(as.matrix(Taxa),as.matrix(paste(GD[,1],"-1",sep="")),as.matrix(paste(GD[,1],"-2",sep="")))
#col.name=t(as.matrix(GbyE.GM[,1]))
#GbyE.GD=rbind(col.name,apply(GbyE.GD,2,as.numeric))
#GbyE.GD=data.frame(col.name,GbyE.GD)
#col.name=t(as.matrix(as.character(GbyE.GM[,1])))
#lm.GD=data.frame(matrix(NA,nrow(GbyE.GD)+1,ncol(GbyE.GD)))
#lm.GD[1,]=col.name
#lm.GD[-1,]=GbyE.GD
#GbyE.GD=as.martix(lm.GD)
GbyE.GD=cbind(row.name,GbyE.GD)
colnames(GbyE.GD)=c(paste(as.character(Taxa)),as.character(GbyE.GM[,1]))
#GbyE.GD=rbind(col.name,apply(GbyE.GD,2,as.numeric))
#GbyE.GD=cbind(row.name,GbyE.GD)
#GbyE.GD=data.frame(GbyE.GD)
#col.names=as.character(GbyE.GD[1,])
#GbyE.GD=GbyE.GD[-1,]
#colnames(GbyE.GD)=col.names
#rm(col.name)
#rm(row.name)
#转化字符串
GbyE.GD[,1]=as.character(GbyE.GD[,1])
#转化数值型
#if(is.numeric(GbyE.GD[,-1])==FALSE){
#GbyE.GD[,-1]=apply(GbyE.GD[,-1],1,as.numeric)
#}
if(file.output==TRUE){
fwrite(GbyE.GD,paste(getwd(),"/","GbyE.GD.txt",sep=""),row.names=F,col.names=T,sep="\t",quote=FALSE)
fwrite(GbyE.GM,paste(getwd(),"/","GbyE.GM.txt",sep=""),row.names=F,col.names=T,sep="\t",quote=FALSE)
}else{
print("~~~~~~~~~The GbyE file is not output~~~~~~~~~~")
print("~~~~~~~~~If you want the file output~~~~~~~~~~")
print("~~~~~~~~~Please set file.output=TRUE~~~~~~~~~~")
}
#data=list(GbyE.GD,GbyE.GM)
#names(data)=c("GbyE.GD","GbyE.GM")
return(list(GbyE.GD=GbyE.GD,GbyE.GM=GbyE.GM))
}
`GAPIT.Multiple.Manhattan` <-
function(model_store,DPP=50000,chor_taxa=NULL,cutOff=0.01,band=5,seqQTN=NULL,byTraits=FALSE,
Y.names=NULL,GM=NULL,interQTN=NULL,WS=10e5,outpch=NULL,inpch=NULL,
plot.style="Oceanic",plot.line=TRUE,plot.type=c("h","s","w")){
#Object: Make a Manhattan Plot
#Output: pdfs of the Multiple Manhattan Plot
#Authors: Zhiwu Zhang and Jiabo Wang
# Last update: AUG 24, 2022
##############################################################################################
Nenviron=length(model_store)*length(Y.names)
environ_name=NULL
new_xz=NULL
if(byTraits)
{
for(i in 1:length(Y.names))
{
for(j in 1:length(model_store))
{
# environ_name=c(environ_name,paste(model_store[i],".",Y.names[j],sep=""))
environ_name=c(environ_name,paste(model_store[j],".",Y.names[i],sep=""))
}
}
}else{
for(i in 1:length(model_store))
{
for(j in 1:length(Y.names))
{
# environ_name=c(environ_name,paste(model_store[i],".",Y.names[j],sep=""))
environ_name=c(environ_name,paste(model_store[i],".",Y.names[j],sep=""))
}
}
}
sig_pos=NULL
simulation=FALSE
if(!is.null(seqQTN)){
#seqQTN=-seqQTN
simulation=TRUE
}
themax.y0=NULL
store.x=NULL
y_filter0=NULL
for(i in 1:length(environ_name))
{
print(paste("Reading GWAS result with ",environ_name[i],sep=""))
environ_result=read.csv(paste("GAPIT.Association.GWAS_Results.",environ_name[i],".csv",sep=""),head=T)
environ_result=environ_result[order(environ_result[,3]),]
environ_result=environ_result[order(environ_result[,2]),]
environ_filter=environ_result[!is.na(environ_result[,4]),]
themax.y=round(max(-log10(environ_filter[,4])),0)
themax.y0=round(max(c(themax.y,themax.y0)),0)
chm.to.analyze <- unique(environ_result[,2])
nchr=length(chm.to.analyze)
# xx=environ_result[,-1]
# ticks=NULL
# lastbase=0
# max.x=NULL
# for(ii in chm.to.analyze)
# {
# index=(xx[,1]==ii)
# ticks <- c(ticks, lastbase+mean(xx[index,2]))
# xx[index,2]=xx[index,2]+lastbase
# lastbase=max(xx[index,2])
# max.x=c(max.x,max(as.numeric(xx[index,2])))
# }
# max.x=c(min(as.numeric(xx[,2])),max.x)
# store.x=c(store.x,as.numeric(xx[,2]))
y_filter=environ_filter[environ_filter[,4]<(cutOff/(nrow(environ_filter))),,drop=FALSE]
traits=environ_name[i]
# print(head(y_filter))
# print(traits)
if(nrow(y_filter)>0)y_filter=cbind(y_filter[,1:5],traits)
y_filter0=rbind(y_filter0,y_filter)
# write.table(y_filter,paste("GAPIT.Filter_",environ_name[i],"_GWAS_result.txt",sep=""))
result=environ_result[,1:4]
result=result[match(as.character(GM[,1]),as.character(result[,1])),]
rownames(result)=1:nrow(result)
#print(i)
if(i==1){
result0=result
colnames(result0)[4]=environ_name[i]
}
if(i!=1){
result0=merge(result0,result[,c(1,4)],by.x=colnames(result0)[1],by.y=colnames(result)[1])
colnames(result0)[i+3]=environ_name[i]
}
rownames(result)=1:nrow(result)
result[is.na(result[,4]),4]=1
# map_store=max.x
sig_pos=append(sig_pos,as.numeric(rownames(result[result[!is.na(result[,4]),4]<(cutOff/nrow(result)),,drop=FALSE])))
}
write.csv(y_filter0,paste("GAPIT.Association.Filter_GWAS_results.csv",sep=""),quote=FALSE)
# print(sig_pos)
#if(length(sig_pos)!=0)sig_pos=sig_pos[!duplicated(sig_pos)]
if(length(sig_pos[!is.na(sig_pos)])>1)
{
# { x_matrix=as.matrix(table(sig_pos))
# x_matrix=cbind(as.data.frame(rownames(x_matrix)),x_matrix)
#print(x_matrix)
lastbase=0
map_store=cbind(as.data.frame(GM[,2]),as.numeric(GM[,3]))
ticks=NULL
# print(head(map_store))
max.x=NULL
for (j in unique(map_store[,1]))
{
index=map_store[,1]==j
ticks <- c(ticks, lastbase+mean(map_store[index,2]))
map_store[index,2]=as.numeric(map_store[index,2])+lastbase
lastbase=max(as.numeric(map_store[index,2]))
max.x=c(max.x,max(as.numeric(map_store[index,2])))
}
# print(ticks)
max.x=c(min(as.numeric(map_store[,2])),max.x)
store.x=c(store.x,as.numeric(map_store[,2]))
# colnames(x_matrix)=c("pos","times")
new_xz0=cbind(sig_pos,map_store[as.numeric(as.character(sig_pos)),,drop=FALSE])
common=as.numeric(new_xz0[,3])
scom=sort(common)
de.sc=scom[-1]-scom[-length(scom)]
dayu1.index=duplicated(scom)|c(abs(de.sc)<WS,FALSE)
print(table(dayu1.index))
if(sum(dayu1.index)>0)
{
scom2=scom[dayu1.index]
# scom2=scom2[!duplicated(scom2)]
# print(new_xz0)
# print(scom2)
sc.index=as.character(new_xz0[,3])%in%scom2
# print(table(sc.index))
new_xz=new_xz0[sc.index,,drop=FALSE]
# print(new_xz)
new_xz=cbind(new_xz[,1],2,new_xz[,-1])
new_xz[duplicated(new_xz[,4]),2]=1
colnames(new_xz)=c("pos","times","chro","xlab")
new_xz=new_xz[!duplicated(new_xz),]
new_xz=as.matrix(new_xz)
new_xz=new_xz[new_xz[,2]!="0",]
new_xz=matrix(as.numeric(new_xz),length(as.vector(new_xz))/4,4)
}
# print(head(new_xz))
}else{
lastbase=0
map_store=cbind(as.data.frame(GM[,2]),as.numeric(GM[,3]))
ticks=NULL
max.x=NULL
# print(head(map_store))
for (j in unique(map_store[,1]))
{
index=map_store[,1]==j
ticks <- c(ticks, lastbase+mean(map_store[index,2]))
map_store[index,2]=as.numeric(map_store[index,2])+lastbase
lastbase=max(as.numeric(map_store[index,2]))
max.x=c(max.x,max(as.numeric(map_store[index,2])))
}
max.x=c(min(as.numeric(map_store[,2])),max.x)
store.x=c(store.x,as.numeric(map_store[,2]))
}
# print(new_xz)
# setup colors
# print(head(result))
# chm.to.analyze <- unique(result[,2])
nchr=length(chm.to.analyze)
size=1 #1
ratio=10 #5
base=1 #1
numCHR=nchr
numMarker=nrow(GM)
bonferroniCutOff=-log10(cutOff/numMarker)
ncycle=ceiling(nchr/5)
ncolor=band*ncycle
thecolor=seq(1,nchr,by= ncycle)
col.Rainbow=rainbow(ncolor+1)
col.FarmCPU=rep(c("#CC6600","deepskyblue","orange","forestgreen","indianred3"),ceiling(numCHR/5))
col.Rushville=rep(c("orangered","navyblue"),ceiling(numCHR/2))
col.Congress=rep(c("deepskyblue3","firebrick"),ceiling(numCHR/2))
col.Ocean=rep(c("steelblue4","cyan3"),ceiling(numCHR/2))
col.PLINK=rep(c("gray10","gray70"),ceiling(numCHR/2))
col.Beach=rep(c("turquoise4","indianred3","darkolivegreen3","red","aquamarine3","darkgoldenrod"),ceiling(numCHR/5))
col.Oceanic=rep(c( '#EC5f67', '#FAC863', '#99C794', '#6699CC', '#C594C5'),ceiling(numCHR/5))
col.cougars=rep(c( '#990000', 'dimgray'),ceiling(numCHR/2))
if(plot.style=="Rainbow")plot.color= col.Rainbow
if(plot.style =="FarmCPU")plot.color= col.Rainbow
if(plot.style =="Rushville")plot.color= col.Rushville
if(plot.style =="Congress")plot.color= col.Congress
if(plot.style =="Ocean")plot.color= col.Ocean
if(plot.style =="PLINK")plot.color= col.PLINK
if(plot.style =="Beach")plot.color= col.Beach
if(plot.style =="Oceanic")plot.color= col.Oceanic
if(plot.style =="cougars")plot.color= col.cougars
if("h"%in%plot.type)
{
Max.high=6*Nenviron
if(Max.high>8)Max.high=40
pdf(paste("GAPIT.Association.Manhattans_High",".pdf" ,sep = ""), width = 20,height=6*Nenviron)
par(mfrow=c(Nenviron,1))
mypch=1
for(k in 1:Nenviron)
{
if(k==Nenviron)
{
par(mar = c(3.5,8,0,8))
# par(pin=c(10,((8-mtext.h)/Nenviron)+mtext.h))
}else{
#par(mfrow=c(Nenviron,1))
par(mar = c(1.5,8,0.5,8))
}
environ_result=read.csv(paste("GAPIT.Association.GWAS_Results.",environ_name[k],".csv",sep=""),head=T)
result=environ_result[,1:4]
result=result[order(result[,3]),]
result=result[order(result[,2]),]
result=result[match(as.character(GM[,1]),as.character(result[,1])),]
rownames(result)=1:nrow(result)
GI.MP=result[,c(2:4)]
borrowSlot=4
GI.MP[,borrowSlot]=0 #Inicial as 0
GI.MP[,5]=1:(nrow(GI.MP))
GI.MP[,6]=1:(nrow(GI.MP))
GI.MP <- GI.MP[!is.na(GI.MP[,1]),]
GI.MP <- GI.MP[!is.na(GI.MP[,2]),]
GI.MP[is.na(GI.MP[,3]),3]=1
#Retain SNPs that have P values between 0 and 1 (not na etc)
GI.MP <- GI.MP[GI.MP[,3]>0,]
GI.MP <- GI.MP[GI.MP[,3]<=1,]
#Remove chr 0 and 99
GI.MP <- GI.MP[GI.MP[,1]!=0,]
total_chromo=length(unique(GI.MP[,1]))
# print(dim(GI.MP))
if(!is.null(seqQTN))GI.MP[seqQTN,borrowSlot]=1
numMarker=nrow(GI.MP)
GI.MP[,3] <- -log10(GI.MP[,3])
GI.MP[,5]=1:numMarker
y.lim <- ceiling(max(GI.MP[,3]))
chm.to.analyze <- unique(GI.MP[,1])
nchr=length(chm.to.analyze)
GI.MP[,6]=1:(nrow(GI.MP))
MP_store=GI.MP
index_GI=MP_store[,3]>=0
MP_store <- MP_store[index_GI,]
ticks=NULL
lastbase=0
# print(head(MP_store))
for(i in chm.to.analyze)
{
index=(MP_store[,1]==i)
ticks <- c(ticks, lastbase+mean(MP_store[index,2]))
MP_store[index,2]=MP_store[index,2]+lastbase
lastbase=max(MP_store[index,2])
}
x0 <- as.numeric(MP_store[,2])
y0 <- as.numeric(MP_store[,3])
z0 <- as.character(MP_store[,1])
# convert chromosome character to number
chor_taxa=as.character(unique(MP_store[,1]))
chor_taxa=chor_taxa[order(as.numeric(as.character(chor_taxa)))]
chr_letter=grep("[A-Z]|[a-z]",chor_taxa)
if(!setequal(integer(0),chr_letter))
{
z0=as.character(MP_store[,1])
for(i in 1:(length(chor_taxa)))
{
index=z0==chor_taxa[i]
z0[index]=i
}
}
z0=as.numeric(z0)
# print(ticks)
x1=sort(x0)
position=order(y0,decreasing = TRUE)
values=y0[position]
if(length(values)<=DPP)
{
index=position[c(1:length(values))]
}else{
# values=sqrt(values) #This shift the weight a little bit to the low building.
#Handler of bias plot
cut0=ceiling(-log10(cutOff/length(values))/2)
rv=runif(length(values))
values=values+rv*(values+cut0)
index=position[which(values>cut0)]
}
x=x0[index]
y=y0[index]
z=z0[index]
#Extract QTN
QTN=MP_store[which(MP_store[,borrowSlot]==1),]
#Draw circles with same size and different thikness
themax=ceiling(max(y))
themax2=ceiling((ceiling(themax/4))*4)
themin=floor(min(y))
wd=((y-themin+base)/(themax-themin+base))*size*ratio
s=size-wd/ratio/2
plot(y~x,xlab="",ylab="" ,ylim=c(0,themax2),xlim=c(min(x),max(x)),
cex.axis=4, cex.lab=4, ,col=plot.color[z],axes=FALSE,type = "p",
pch=mypch,lwd=wd,cex=s+2.5,cex.main=2)
mtext(side=2,expression(-log[10](italic(p))),line=3.5, cex=2.5)
if(!simulation)
{
abline(v=QTN[2], lty = 2, lwd=1.5, col = "grey")}else{
points(QTN[,2], QTN[,3], pch=21, cex=2.8,lwd=1.5,col="dimgrey")
points(QTN[,2], QTN[,3], pch=20, cex=1.8,lwd=1.5,col="dimgrey")
}
if(plot.line)
{
if(!is.null(nrow(new_xz)))
{
abline(v=as.numeric(new_xz[,4]),col="grey",lty=as.numeric(new_xz[,2]),untf=T,lwd=3)
}else{
abline(v=as.numeric(new_xz[1]),col=plot.color[as.numeric(new_xz[3])],lty=as.numeric(new_xz[2]),untf=T,lwd=3)
}
}
#Add a horizontal line for bonferroniCutOff
# print(ticks)
abline(h=bonferroniCutOff,lty=1,untf=T,lwd=3,col="forestgreen")
axis(2, yaxp=c(0,themax2,4),cex.axis=2.3,tick=T,las=1,lwd=2.5)
if(k==Nenviron)axis(1, at=max.x,cex.axis=2.5,labels=rep("",length(max.x)),tick=T,lwd=2.5)
if(k==Nenviron)axis(1, at=ticks,cex.axis=2.5,labels=chm.to.analyze,tick=F,line=1)
mtext(side=4,paste(environ_name[k],sep=""),line=3.2,cex=2)
}#end of environ_name
dev.off()
}#end of plot.type
if("w"%in%plot.type)
{
pdf(paste("GAPIT.Association.Manhattans_Wide",".pdf" ,sep = ""), width = 16,height=8.5)
par(mfrow=c(Nenviron,1))
mtext.h=0.5
size=2
ratio=5
for(k in 1:Nenviron)
{
if(k==Nenviron)
{#par(mfrow=c(Nenviron,1))
# print(par())
par(mar = c(2.5,8,0,8))
# par(pin=c(10,((8-mtext.h)/Nenviron)+mtext.h))
}else{
#par(mfrow=c(Nenviron,1))
par(mar = c(2,8,0.5,8))
# par(pin=c(10,(8-mtext.h)/Nenviron))
}
environ_result=read.csv(paste("GAPIT.Association.GWAS_Results.",environ_name[k],".csv",sep=""),head=T)
#print(environ_result[as.numeric(new_xz[,1]),])
result=environ_result[,1:4]
result=result[order(result[,3]),]
result=result[order(result[,2]),]
result=result[match(as.character(GM[,1]),as.character(result[,1])),]
rownames(result)=1:nrow(result)
GI.MP=result[,c(2:4)]
borrowSlot=4
GI.MP[,borrowSlot]=0 #Inicial as 0
GI.MP[,5]=1:(nrow(GI.MP))
GI.MP[,6]=1:(nrow(GI.MP))
GI.MP <- GI.MP[!is.na(GI.MP[,1]),]
GI.MP <- GI.MP[!is.na(GI.MP[,2]),]
GI.MP[is.na(GI.MP[,3]),3]=1
#Retain SNPs that have P values between 0 and 1 (not na etc)
GI.MP <- GI.MP[GI.MP[,3]>0,]
GI.MP <- GI.MP[GI.MP[,3]<=1,]
#Remove chr 0 and 99
GI.MP <- GI.MP[GI.MP[,1]!=0,]
total_chromo=length(unique(GI.MP[,1]))
# print(dim(GI.MP))
if(!is.null(seqQTN))GI.MP[seqQTN,borrowSlot]=1
numMarker=nrow(GI.MP)
bonferroniCutOff=-log10(cutOff/numMarker)
GI.MP[,3] <- -log10(GI.MP[,3])
GI.MP[,5]=1:numMarker
y.lim <- ceiling(max(GI.MP[,3]))
chm.to.analyze <- unique(GI.MP[,1])
# chm.to.analyze=chm.to.analyze[order(chm.to.analyze)]
nchr=length(chm.to.analyze)
GI.MP[,6]=1:(nrow(GI.MP))
MP_store=GI.MP
index_GI=MP_store[,3]>=0
MP_store <- MP_store[index_GI,]
ticks=NULL
lastbase=0
for (i in chm.to.analyze)
{
index=(MP_store[,1]==i)
ticks <- c(ticks, lastbase+mean(MP_store[index,2]))
MP_store[index,2]=MP_store[index,2]+lastbase
lastbase=max(MP_store[index,2])
}
x0 <- as.numeric(MP_store[,2])
y0 <- as.numeric(MP_store[,3])
z0 <- as.character(MP_store[,1])
# convert chromosome character to number
chor_taxa=as.character(unique(MP_store[,1]))
chor_taxa=chor_taxa[order(as.numeric(as.character(chor_taxa)))]
chr_letter=grep("[A-Z]|[a-z]",chor_taxa)
if(!setequal(integer(0),chr_letter))
{
z0=as.character(MP_store[,1])
for(i in 1:(length(chor_taxa)))
{
index=z0==chor_taxa[i]
z0[index]=i
}
}
z0=as.numeric(z0)
x1=sort(x0)
position=order(y0,decreasing = TRUE)
values=y0[position]
if(length(values)<=DPP)
{
index=position[c(1:length(values))]
}else{
# values=sqrt(values) #This shift the weight a little bit to the low building.
#Handler of bias plot
cut0=ceiling(-log10(cutOff/length(values))/2)
rv=runif(length(values))
values=values+rv*(values+cut0)
index=position[which(values>cut0)]
}
x=x0[index]
y=y0[index]
z=z0[index]
# print(length(x))
#Extract QTN
#if(!is.null(seqQTN))MP_store[seqQTN,borrowSlot]=1
#if(!is.null(interQTN))MP_store[interQTN,borrowSlot]=2
QTN=MP_store[which(MP_store[,borrowSlot]==1),]
#Draw circles with same size and different thikness
themax=ceiling(max(y))
themax2=ceiling((ceiling(themax/4))*4)
themin=floor(min(y))
# size=5
wd=((y-themin+base)/(themax-themin+base))*size*ratio
# wd=0.5
s=size-wd/ratio/2
mypch=1
bamboo=4
# plot(y~x,xlab="",ylab="" ,ylim=c(0,themax),
# cex.axis=4, cex.lab=4, ,col=plot.color[z],axes=FALSE,type = "p",pch=mypch,lwd=0.5,cex=0.7,cex.main=2)
plot(y~x,xlab="",ylab="" ,ylim=c(0,themax2),xlim=c(min(x),max(x)),
cex.axis=4, cex.lab=4, ,col=plot.color[z],axes=FALSE,type = "p",
pch=mypch,lwd=wd,cex=s,cex.main=2)
mtext(side=2,expression(-log[10](italic(p))),line=3, cex=1)
if(plot.line)
{
if(!is.null(nrow(new_xz))) {abline(v=as.numeric(new_xz[,4]),col="grey",lty=as.numeric(new_xz[,2]),untf=T,lwd=2)
}else{abline(v=as.numeric(new_xz[1]),col=plot.color[as.numeric(new_xz[3])],lty=as.numeric(new_xz[2]),untf=T,lwd=2)
}
}
if(!simulation){abline(v=QTN[2], lty = 2, lwd=1.5, col = "grey")}else{
# print("$$$")
points(QTN[,2], QTN[,3], type="p",pch=21, cex=2.8,lwd=1.5,col="dimgrey")
points(QTN[,2], QTN[,3], type="p",pch=20, cex=1.5,lwd=1.5,col="dimgrey")
}
#Add a horizontal line for bonferroniCutOff
abline(h=bonferroniCutOff,lty=1,untf=T,lwd=1,col="forestgreen")
axis(2, yaxp=c(0,themax2,bamboo),cex.axis=1.5,las=1,tick=F)
if(k==Nenviron)axis(1, at=ticks,cex.axis=1.5,line=0.001,labels=chm.to.analyze,tick=F)
mtext(side=4,paste(environ_name[k],sep=""),line=2,cex=1,base_family="Arial")
box()
}#end of environ_name
dev.off()
}#end of plot.type
if("s"%in%plot.type)
{
# wd=((y-themin+base)/(themax-themin+base))*size*ratio
wd=2
if(is.null(outpch))
{
allpch0=c(0,1,2,5,6)
}else{
allpch0=outpch
}
if(is.null(inpch))
{
add.pch=c("+","*","-","#","<",">","^","$","=","|","?",as.character(1:9),letters[1:26],LETTERS[1:26])
}else{
add.pch=inpch
}
n.vals=ceiling(Nenviron/length(allpch0))-1
s=size-wd/ratio/2
DPP=500
pdf(paste("GAPIT.Association.Manhattans_Symphysic",".pdf" ,sep = ""), width = 30,height=18)
par(mfrow=c(1,1))
par(mar = c(5,8,5,1))
themax.y02=ceiling((ceiling(themax.y0/4))*4)
plot(1~1,col="white",xlab="",ylab="" ,ylim=c(0,themax.y02),xlim=c(min(store.x,na.rm=TRUE),max(store.x,na.rm=TRUE)),yaxp=c(0,themax.y02,4),
cex.axis=4, cex.lab=4,axes=FALSE,
pch=1,cex.main=4)
# print(ticks)
#Add a horizontal line for bonferroniCutOff
axis(1, at=max.x,cex.axis=2,labels=rep("",length(max.x)),tick=T,lwd=2.5)
axis(1, at=ticks,cex.axis=2,labels=chm.to.analyze,tick=F,line=1)
axis(2, yaxp=c(0,themax.y02,4),cex.axis=2,tick=T,las=1,lwd=2.5)
if(!is.null(cutOff))abline(h=bonferroniCutOff,lty=1,untf=T,lwd=3,col="forestgreen")
if(plot.line)
{
if(!is.null(nrow(new_xz)))
{
abline(v=as.numeric(new_xz[,4]),col="grey",lty=as.numeric(new_xz[,2]),untf=T,lwd=3)
}else{
abline(v=as.numeric(new_xz[1]),col=plot.color[as.numeric(new_xz[3])],lty=as.numeric(new_xz[2]),untf=T,lwd=3)
}
}
mtext(side=2,expression(-log[10](italic(p))),line=4, cex=2.5)
# legend("top",legend=paste(environ_name,sep=""),ncol=length(environ_name),
# col="black",pch=allpch[1:Nenviron],lty=0,lwd=1,cex=2,
# bty = "o", bg = "white",box.col="white")
# step.vals=0
# if(1>2){
for(k in 1:Nenviron)
{
step.vals=ceiling(k/length(allpch0))-1
environ_result=read.csv(paste("GAPIT.Association.GWAS_Results.",environ_name[k],".csv",sep=""),head=T)
result=environ_result[,1:4]
result=result[order(result[,3]),]
result=result[order(result[,2]),]
result=result[match(as.character(GM[,1]),as.character(result[,1])),]
rownames(result)=1:nrow(result)
GI.MP=result[,c(2:4)]
borrowSlot=4
GI.MP[,borrowSlot]=0 #Inicial as 0
GI.MP[,5]=1:(nrow(GI.MP))
GI.MP[,6]=1:(nrow(GI.MP))
GI.MP <- GI.MP[!is.na(GI.MP[,1]),]
GI.MP <- GI.MP[!is.na(GI.MP[,2]),]
GI.MP[is.na(GI.MP[,3]),3]=1
#Retain SNPs that have P values between 0 and 1 (not na etc)
GI.MP <- GI.MP[GI.MP[,3]>0,]
GI.MP <- GI.MP[GI.MP[,3]<=1,]
#Remove chr 0 and 99
GI.MP <- GI.MP[GI.MP[,1]!=0,]
total_chromo=length(unique(GI.MP[,1]))
# print(dim(GI.MP))
if(!is.null(seqQTN))GI.MP[seqQTN,borrowSlot]=1
numMarker=nrow(GI.MP)
bonferroniCutOff=-log10(cutOff/numMarker)
GI.MP[,3] <- -log10(GI.MP[,3])
GI.MP[,5]=1:numMarker
y.lim <- ceiling(max(GI.MP[,3]))
chm.to.analyze <- unique(GI.MP[,1])
# print(chm.to.analyze)
# chm.to.analyze=chm.to.analyze[order(chm.to.analyze)]
nchr=length(chm.to.analyze)
# print(chm.to.analyze)
GI.MP[,6]=1:(nrow(GI.MP))
MP_store=GI.MP
index_GI=MP_store[,3]>=0
MP_store <- MP_store[index_GI,]
ticks=NULL
lastbase=0
for (i in chm.to.analyze)
{
index=(MP_store[,1]==i)
ticks <- c(ticks, lastbase+mean(MP_store[index,2]))
MP_store[index,2]=MP_store[index,2]+lastbase
lastbase=max(MP_store[index,2])
}
x0 <- as.numeric(MP_store[,2])
y0 <- as.numeric(MP_store[,3])
z0 <- as.character(MP_store[,1])
# convert chromosome character to number
chor_taxa=as.character(unique(MP_store[,1]))
chor_taxa=chor_taxa[order(as.numeric(as.character(chor_taxa)))]
chr_letter=grep("[A-Z]|[a-z]",chor_taxa)
if(!setequal(integer(0),chr_letter))
{
z0=as.character(MP_store[,1])
for(i in 1:(length(chor_taxa)))
{
index=z0==chor_taxa[i]
z0[index]=i
}
}
z0=as.numeric(z0)
max.x=NULL
for (i in chm.to.analyze)
{
index=(MP_store[,1]==i)
max.x=c(max.x,max(x0[index]))
}
max.x=c(min(x0),max.x)
x1=sort(x0)
position=order(y0,decreasing = TRUE)
values=y0[position]
if(length(values)<=DPP)
{
index=position[c(1:length(values))]
}else{
# values=sqrt(values) #This shift the weight a little bit to the low building.
#Handler of bias plot
cut0=ceiling(-log10(cutOff/length(values))/2)
rv=runif(length(values))
values=values+rv*(values+cut0)
index=position[which(values>cut0)]
}
x=x0[index]
y=y0[index]
z=z0[index]
# print(length(x))
#Extract QTN
#if(!is.null(seqQTN))MP_store[seqQTN,borrowSlot]=1
#if(!is.null(interQTN))MP_store[interQTN,borrowSlot]=2
QTN=MP_store[which(MP_store[,borrowSlot]==1),]
#Draw circles with same size and different thikness
themax=ceiling(max(y))
# themax.y02=ceiling((ceiling(themax.y0/4)+1)*4)
# print(themax.y02)
themin=floor(min(y))
mypch=allpch0[k-step.vals*length(allpch0)]
# if(k!=1) par(new=T)
par(new=T)
plot(y~x,xlab="",ylab="" ,ylim=c(0,themax.y02),xlim=c(min(x),max(x)),yaxp=c(0,themax.y02,4),
cex.axis=4, cex.lab=4,col=plot.color[z],axes=FALSE,
pch=mypch,lwd=1,cex=s+2.5,cex.main=4)
if(step.vals!=0)
{
points(y~x,pch=add.pch[step.vals],col=plot.color[z],cex=s+0.5,cex.main=4)
}
if(!simulation)
{
abline(v=QTN[2], lty = 2, lwd=1.5, col = "grey")
}else{
points(QTN[,2], QTN[,3], pch=20, cex=2,lwd=2.5,col="dimgrey")
}
}#end of environ_name
dev.off()
# }
## Plot legend
nchar.traits=1.5
# environ_name=paste(environ_name,"1234",sep="")
nchar0=max(nchar(environ_name))
print(nchar0)
if(Nenviron>5)
{
yourpch=c(rep(allpch0,n.vals),allpch0[1:(Nenviron-length(allpch0)*n.vals)])
}else{
yourpch=allpch0[1:Nenviron]
}
yourpch2=NULL
for(pp in 1:n.vals)
{
yourpch2=c(yourpch2,rep(add.pch[pp],length(allpch0)))
}
# yourpch2=c(rep(allpch0,n.vals),allpch0[Nenviron-length(allpch0)*n.vals])
if(Nenviron>5){
yourpch2=yourpch2[1:(Nenviron-length(allpch0))]
yourpch2=c(rep(NA,length(allpch0)),yourpch2)
}
max.row=25
max.pch=ifelse(Nenviron<max.row,Nenviron,max.row)
n.col.pch=ceiling(Nenviron/max.row)
ratio.cex=ceiling(Nenviron/5)
if(ratio.cex>5)ratio.cex=5
cex.Ne=ratio.cex*(0.05*ratio.cex+0.35) #the size of cex
if(ratio.cex<3)cex.Ne=1
c.t.d=c(0.5,1,1,1,1.5)[ratio.cex] # the different between size of cex and text
cex.di=0.3*ratio.cex # the different size between cex and signal
text.di=c(.02,0.01,0.01,0.02,0.02)[ratio.cex] #the different distance between cex and text
high.Ne=2*ratio.cex # the total highth of figure
cex.betw=c(.38,.5,.7,0.9,1)[ratio.cex] # distance between cexes
x.di=c(1.12,1.09,0.5,0.8,1.3)[ratio.cex]/2 # distance between markers in x axis
# print(nchar0)
# x.di0=c(0)
if(n.col.pch>1){
text.di=(0.01*nchar0)/3+0.02
# x.di=0.52*n.col.pch
x.di=(0.1*(ceiling(nchar0/5)-1)+(n.col.pch-1)*0.1)*ceiling(nchar0/5)#*n.col.pch
}
# print(x.di)
# if(Nenviron>5){
# cex.Ne=3
# cex.di=1.5
# text.di=.02
# high.Ne=10
# cex.betw=0.9
# }else{
# cex.Ne=1
# cex.di=0.3
# high.Ne=Nenviron/2
# text.di=.02
# cex.betw=0.9
# }
write.csv(environ_name,"GAPIT.Association.Manhattans_Symphysic_Traitsnames.csv",quote=FALSE)
pdf(paste("GAPIT.Association.Manhattans_Symphysic_Legend",".pdf" ,sep = ""), width = 4+(x.di*(n.col.pch+1)),height=high.Ne)
par(mfrow=c(1,1))
par(mar = c(cex.Ne+1,2,cex.Ne+1,2))
# print(length(yourpch))
# print(length(yourpch2))
plot(0,0,xlab="",ylab="" ,axes=FALSE,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),col="white")
for(kk in 1:n.col.pch)
{
par(new=T)
if(kk==n.col.pch)
{
if(n.col.pch==1)
{
# print(kk)
max.pch2=Nenviron-(n.col.pch-1)*max.row
plot(rep(0,max.pch2),(max.pch:(max.pch-max.pch2+1))*cex.betw,xlab="",ylab="" ,axes=FALSE,col="black",
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne,
pch=yourpch[((kk-1)*max.row+1):Nenviron])
if(Nenviron>5) points(rep(0,max.pch2),((max.pch):(max.pch-max.pch2+1))*cex.betw,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne-cex.di,
pch=yourpch2[((kk-1)*max.row+1):Nenviron])
text(rep((0+text.di),max.pch2),(max.pch:(max.pch-max.pch2+1))*cex.betw,labels=environ_name[((kk-1)*max.row+1):Nenviron],pos=4,cex=cex.Ne-c.t.d)
}else{
# print(kk)
max.pch2=Nenviron-(n.col.pch-1)*max.row
plot(rep((kk-1)*x.di,max.pch2),(max.pch:(max.pch-max.pch2+1))*cex.betw,xlab="",ylab="" ,axes=FALSE,col="black",
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne,
pch=yourpch[((kk-1)*max.row+1):Nenviron])
if(Nenviron>5) points(rep((kk-1)*x.di,max.pch2),((max.pch):(max.pch-max.pch2+1))*cex.betw,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne-cex.di,
pch=yourpch2[((kk-1)*max.row+1):Nenviron])
text(rep(((kk-1)*x.di+text.di),max.pch2),(max.pch:(max.pch-max.pch2+1))*cex.betw,labels=environ_name[((kk-1)*max.row+1):Nenviron],pos=4,cex=cex.Ne-c.t.d)
}
}else{
if(kk==1)
{
print(kk)
plot(rep(0,max.pch),(max.pch:1)*cex.betw,xlab="",ylab="" ,axes=FALSE,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne,
pch=yourpch[((kk-1)*max.row+1):((kk-1)*max.row+max.row)])
if(Nenviron>5) points(rep(0,max.pch),((max.pch):1)*cex.betw,
xlim=c(1,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne-cex.di,
pch=yourpch2[((kk-1)*max.row+1):((kk-1)*max.row+max.row)])
text(rep((0+text.di),max.pch),(max.pch:1)*cex.betw,labels=environ_name[((kk-1)*max.row+1):((kk-1)*max.row+max.row)],pos=4,cex=cex.Ne-c.t.d)
}else{
print(kk)
plot(rep((kk-1)*x.di,max.pch),(max.pch:1)*cex.betw,xlab="",ylab="" ,axes=FALSE,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne,
pch=yourpch[((kk-1)*max.row+1):((kk-1)*max.row+max.row)])
if(Nenviron>5) points(rep((kk-1)*x.di,max.pch),((max.pch):1)*cex.betw,
xlim=c(0,x.di*(n.col.pch)),ylim=c(0,max.pch),lwd=1,cex=cex.Ne-cex.di,
pch=yourpch2[((kk-1)*max.row+1):((kk-1)*max.row+max.row)])
text(rep(((kk-1)*x.di+text.di),max.pch),(max.pch:1)*cex.betw,labels=environ_name[((kk-1)*max.row+1):((kk-1)*max.row+max.row)],pos=4,cex=cex.Ne-c.t.d)
}
}
}#end of plot.type
dev.off()
}
print("GAPIT.Association.Manhattans has done !!!")
return(list(multip_mapP=result0,xz=new_xz))
} #end of GAPIT.Manhattan
#=============================================================================================
########## These three functions come from MVP package, Jiabo did some modifications
########## Following Apache License, we thank MVP developper to build these functions.
########## 1 creat P value scale in addtitional chromsome
########## 2 set col same as GAPIT
########## 3
circle.plot <- function(myr,type="l",x=NULL,lty=1,lwd=1,col="black",add=TRUE,n.point=1000)
{
graphics::curve(sqrt(myr^2-x^2),xlim=c(-myr,myr),n=n.point,ylim=c(-myr,myr),type=type,lty=lty,col=col,lwd=lwd,add=add)
graphics::curve(-sqrt(myr^2-x^2),xlim=c(-myr,myr),n=n.point,ylim=c(-myr,myr),type=type,lty=lty,col=col,lwd=lwd,add=TRUE)
}
Densitplot <- function(
map,
col=c("darkblue", "white", "red"),
main="SNP Density",
bin=1e6,
band=3,
width=5,
legend.len=10,
legend.max=NULL,
legend.pt.cex=3,
legend.cex=1,
legend.y.intersp=1,
legend.x.intersp=1,
plot=TRUE
)
{ #print(head(map))
map <- as.matrix(map)
map <- map[!is.na(map[, 2]), ]
map <- map[!is.na(map[, 3]), ]
map <- map[map[, 2] != 0, ]
#map <- map[map[, 3] != 0, ]
options(warn = -1)
max.chr <- max(as.numeric(map[, 2]), na.rm=TRUE)
if(is.infinite(max.chr)) max.chr <- 0
map.xy.index <- which(!as.numeric(map[, 2]) %in% c(0 : max.chr))
if(length(map.xy.index) != 0){
chr.xy <- unique(map[map.xy.index, 2])
for(i in 1:length(chr.xy)){
map[map[, 2] == chr.xy[i], 2] <- max.chr + i
}
}
map <- map[order(as.numeric(map[, 2]), as.numeric(map[, 3])), ]
chr <- as.numeric(map[, 2])
pos <- as.numeric(map[, 3])
chr.num <- unique(chr)
#print(chr.num)
chorm.maxlen <- max(pos)
if(plot) plot(NULL, xlim=c(0, chorm.maxlen + chorm.maxlen/10), ylim=c(0, length(chr.num) * band + band), main=main,axes=FALSE, xlab="", ylab="", xaxs="i", yaxs="i")
pos.x <- list()
col.index <- list()
maxbin.num <- NULL
#print(chr.num)
for(i in 1 : length(chr.num)){
pos.x[[i]] <- pos[which(chr == chr.num[i])]
cut.len <- ceiling((max(pos.x[[i]]) - min(pos.x[[i]])) / bin)
if(cut.len <= 1){
col.index[[i]] = 1
}else{
cut.r <- cut(pos.x[[i]], cut.len, labels=FALSE)
eachbin.num <- table(cut.r)
#print(eachbin.num)
maxbin.num <- c(maxbin.num, max(eachbin.num))
col.index[[i]] <- rep(eachbin.num, eachbin.num)
}
}
Maxbin.num <- max(maxbin.num)
maxbin.num <- Maxbin.num
if(!is.null(legend.max)){
maxbin.num <- legend.max
}
#print(col)
#print(maxbin.num)
col = grDevices::colorRampPalette(col)(maxbin.num)
col.seg=NULL
for(i in 1 : length(chr.num)){
if(plot) graphics::polygon(c(0, 0, max(pos.x[[i]]), max(pos.x[[i]])),
c(-width/5 - band * (i - length(chr.num) - 1), width/5 - band * (i - length(chr.num) - 1),
width/5 - band * (i - length(chr.num) - 1), -width/5 - band * (i - length(chr.num) - 1)), col="grey", border="grey")
if(!is.null(legend.max)){
if(legend.max < Maxbin.num){
col.index[[i]][col.index[[i]] > legend.max] <- legend.max
}
}
col.seg <- c(col.seg, col[round(col.index[[i]] * length(col) / maxbin.num)])
if(plot) graphics::segments(pos.x[[i]], -width/5 - band * (i - length(chr.num) - 1), pos.x[[i]], width/5 - band * (i - length(chr.num) - 1),
col=col[round(col.index[[i]] * length(col) / maxbin.num)], lwd=1)
}
if(length(map.xy.index) != 0){
for(i in 1:length(chr.xy)){
chr.num[chr.num == max.chr + i] <- chr.xy[i]
}
}
chr.num <- rev(chr.num)
if(plot) graphics::mtext(at=seq(band, length(chr.num) * band, band),text=paste("Chr", chr.num, sep=""), side=2, las=2, font=1, cex=0.6, line=0.2)
if(plot) graphics::axis(3, at=seq(0, chorm.maxlen, length=10), labels=c(NA, paste(round((seq(0, chorm.maxlen, length=10))[-1] / 1e6, 0), "Mb", sep="")),
font=1, cex.axis=0.8, tck=0.01, lwd=2, padj=1.2)
# image(c(chorm.maxlen-chorm.maxlen * legend.width / 20 , chorm.maxlen),
# round(seq(band - width/5, (length(chr.num) * band + band) * legend.height / 2 , length=maxbin.num+1), 2),
# t(matrix(0 : maxbin.num)), col=c("white", rev(heat.colors(maxbin.num))), add=TRUE)
legend.y <- round(seq(0, maxbin.num, length=legend.len))
len <- legend.y[2]
legend.y <- seq(0, maxbin.num, len)
if(!is.null(legend.max)){
if(legend.max < Maxbin.num){
if(!maxbin.num %in% legend.y){
legend.y <- c(legend.y, paste(">=", maxbin.num, sep=""))
legend.y.col <- c(legend.y[c(-1, -length(legend.y))], maxbin.num)
}else{
legend.y[length(legend.y)] <- paste(">=", maxbin.num, sep="")
legend.y.col <- c(legend.y[c(-1, -length(legend.y))], maxbin.num)
}
}else{
if(!maxbin.num %in% legend.y){
legend.y <- c(legend.y, maxbin.num)
}
legend.y.col <- c(legend.y[-1])
}
}else{
if(!maxbin.num %in% legend.y){
legend.y <- c(legend.y, paste(">", max(legend.y), sep=""))
legend.y.col <- c(legend.y[c(-1, -length(legend.y))], maxbin.num)
}else{
legend.y.col <- c(legend.y[-1])
}
}
legend.y.col <- as.numeric(legend.y.col)
legend.col <- c("grey", col[round(legend.y.col * length(col) / maxbin.num)])
if(plot) graphics::legend(x=(chorm.maxlen + chorm.maxlen/100), y=( -width/2.5 - band * (length(chr.num) - length(chr.num) - 1)), title="", legend=legend.y, pch=15, pt.cex = legend.pt.cex, col=legend.col,
cex=legend.cex, bty="n", y.intersp=legend.y.intersp, x.intersp=legend.x.intersp, yjust=0, xjust=0, xpd=TRUE)
if(!plot) return(list(den.col=col.seg, legend.col=legend.col, legend.y=legend.y))
}
GAPIT.Circle.Manhattan.Plot <- function(
Pmap,
col=c("#377EB8", "#4DAF4A", "#984EA3", "#FF7F00"),
#col=c("darkgreen", "darkblue", "darkyellow", "darkred"),
bin.size=1e6,
bin.max=NULL,
pch=19,
band=1,
cir.band=0.5,
H=1.5,
ylim=NULL,
cex.axis=1,
plot.type="c",
multracks=TRUE,
cex=c(0.5,0.8,1),
r=0.3,