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gapit_functions.txt
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gapit_functions.txt
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`GAPIT.0000` <-
function(){
##############################################################################################
#GAPIT: Genome Association and Prediction Integrated Tool
#Objective 1: State of art methods for high power, accuracy and speed;
#Objective 2: User friendly by design, help documents, and web forum;
#Objective 3: Comprehensive output to interpret data and results;
#Objective 4: Informative tables and high quality figures for reports and publication;
#Methods implimented:
# 1. GLM (Structure or Q method for GWAS, Pritchard et. al. Genetics, 2000)
# 2. MLM (Q+K, Yu et. al. Nature Genetics, 2006)
# 3. gBLUP (Marker based kinship, Zhang et. al. Journal of Animal Science, 2007)
# 4. PCA (Zhao et. al. Plos Genetics, 2007)
# 5. EMMA (Kang et. al. Genetics, 2008)
# 6. CMLM (Zhang et. al. Nature Genetics, 2010)
# 7. EMMAx (Kang et. al. Nature Genetics, 2010)
# 8. P3D (Zhang et. al. Nature Genetics, 2010)
# 9. FaST-LMM (Lippert et. al. Nature Methods, 2011)
# 10. ECMLM (Li et. al. BMC Bioogy, 2014)
# 11. SUPER (Wang et. al. PLoS One, 2014)
#Designed by Zhiwu Zhang
#Authors: Alex Lipka, Feng Tian, Qishan Wang, Xiaolei Liu, Meng Li,You Tang and Zhiwu Zhang
#Citation: Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, and Zhang Z
#GAPIT: genome association and prediction integrated tool. Bioinformatics 2012, 28:2397-2399.
GAPIT.Version="2016.03.01 (Kinship defined by Zhiwu Zhang), http://zzlab.net/GAPIT"
return(GAPIT.Version)
}
#=============================================================================================
#Object: To calculate Area Under (ROC) Curve (AUC)
#Straitegy: NA
#Output: P value
#intput: beta-power and alpha-fdr or type I error
#Authors: Zhiwu Zhang
#Last update: December 18, 2015
##############################################################################################
GAPIT.AUC=function(beta=NULL,alpha=NULL){
n=length(beta)
#plot(alpha,beta,type="b")
db=beta[-1]-beta[-n]
da=1-.5*(alpha[-1]+alpha[-n])
ab=da*db
AUC=sum(ab)
return(AUC)
}
#=============================================================================================
#Object: To generate binary phenotype
#Straitegy: NA
#Output: binary phenotype (0 and 1's)
#intput: genetic effect (x), hertiability (h2) and ratio of 1's (r)
#Authors: Zhiwu Zhang
#Last update: March 18, 2016
##############################################################################################
`GAPIT.BIPH` <-
function(x=0,h2=.5,r=.25){
#To assign probability for given standard normal variable x and h2
#Author: Zhiwu Zhang
#Last update: Febuary 27, 2016
p=pnorm(x)
srp=1-p-r
sh=1/(1-sqrt(h2))
adj=(r-.5)*(1-sqrt(h2))
f=1/(1+exp(sh*srp))+adj
return(f)
}
#=============================================================================================
`GAPIT.Block` <-
function(Z,GA,KG){
#Object: To split a group kinship into two blocks containing individuals with and without phenotype
#Output: GAU,KW,KO,KWO
#Authors: Zhiwu Zhang and Alex Lipka
# Last update: April 14, 2011
##############################################################################################
# To separate group kiship into two blocks: with and without phenotype.
# A group goes to with phenotype as loog as it has one phenotyped individual.
#find position in group assignment (GA) for the individual associate with phenotype (specified by Z)
#taxa=unique(intersect(as.matrix(Z[1,-1]),GA[,1]))
taxa.Z=as.matrix(Z[1,-1])
taxa.GA=as.matrix(GA[,1])
position=taxa.GA%in%taxa.Z
#Initial block as 2
GAU=cbind(GA,2)
#Assign block as 1 if the individual has phenotype
GAU[position,3]=1
#Modify the non-phenotyped individuals if they in a group with phenotyped individuals
#To find the groups with phenotyped individuals
#update block assignment for all these groups
#get list of group that should be block 1
#grp.12=as.matrix(unique(GAU[,2]))
#grp.1=as.matrix(unique(GAU[which(GAU[,3]==1),2]))
#grp.2= as.matrix(setdiff(grp.12,grp.1))
grp.12=as.matrix(as.vector(unique(GAU[,2])) ) #unique group
grp.1=as.matrix(as.vector(unique(GAU[which(GAU[,3]==1),2])) ) #unique phenotyped group
grp.2= as.matrix(as.vector(setdiff(grp.12,grp.1))) #unique unphenotyped group
numWithout=length(grp.2)
order.1=1:length(grp.1)
order.2=1:length(grp.2)
if(numWithout >0) grpblock=as.matrix(rbind(cbind(grp.1,1,order.1), cbind(grp.2, 2, order.2)))
if(numWithout==0) grpblock=as.matrix( cbind(grp.1,1,order.1), )
order.block=order(as.matrix(GAU[,3]))
colnames(grpblock)=c("grp","block","ID")
#Indicators: 1-Phenotype, 1.5- unphenotyped but in a group with other phenotyped, 2-rest (Zhiwu, Dec 7,2012)
#GAU0 <- merge(GAU[order.block,-3], grpblock, by.x = "X2", by.y = "grp")
#GAU=GAU0[,c(2,1,3,4)]
GAU1 <- merge(GAU[order.block,], grpblock, by.x = "X2", by.y = "grp")
#print(GAU1)
GAU1[,4]=(as.numeric(GAU1[,3])+as.numeric(GAU1[,4]))/2
#print(GAU1)
GAU=GAU1[,c(2,1,4,5)]
KW=KG[grp.1,grp.1]
KO=KG[grp.2,grp.2]
KWO=KG[grp.1,grp.2]
#write.table(GAU, "GAU.txt", quote = FALSE, sep = "\t", row.names = TRUE,col.names = TRUE)
#print("GAPIT.Block accomplished successfully!")
return(list(GAU=GAU,KW=KW,KO=KO,KWO=KWO))
}#The function GAPIT.Block ends here
#=============================================================================================
`GAPIT.Bread` <-
function(Y=NULL,CV=NULL,Z=NULL,KI=NULL,GK=NULL,GD=NULL,GM=NULL,
method=NULL,delta=NULL,vg=NULL,ve=NULL,LD=0.01,GTindex=NULL,
file.output=TRUE){
#Object: To calculate p-values of SNPs by using method of GLM, MLM, CMLM, FaST, SUPER and DC
#Straitegy: NA
#Output: GWAS, GPS,REMLs,vg,ve,delta
#intput:
#Y: phenotype with columns of taxa,Y1,Y2...
#CV: covariate variables with columns of taxa, v1,v2...
#GD: same as GK. This is the genotype to screen, the columns are taxa,SNP1,SNP2,...
#GK: Genotype data in numerical format, taxa goes to row and snp go ti columns. the first column is taxa
#GM: Genotype map with columns of snpID,chromosome and position
#method: Options are GLM, MLM, CMLM, FaST, SUPER ,FARM-CPU and DC
#Authors: Zhiwu Zhang
#Last update: November 2, 2011
##############################################################################################
#print("GAPIT.SUPER in progress...")
#Performing first screening with GLM
if(method=="GLM"){
#print("---------------screening by GLM----------------------------------")
myGAPIT <- GAPIT(
Y=Y,
CV=CV,
Z=Z,
KI=KI,
GD=GD,
GM=GM,
group.from=0,
group.to=0,
QC=FALSE,
GTindex=GTindex,
file.output=file.output
)
GWAS=myGAPIT$GWAS
GPS=myGAPIT$GPS
REMLs=myGAPIT$REMLs
delta=myGAPIT$ve/myGAPIT$va
vg=myGAPIT$vg
ve=myGAPIT$ve
}
#Performing first screening with MLM
if(method=="MLM"){
#print("---------------screening by MLM----------------------------------")
myGAPIT <- GAPIT(
Y=Y,
CV=CV,
Z=Z,
KI=KI,
GD=GD,
GM=GM,
group.from=nrow(Y),
group.to=nrow(Y),
QC=FALSE,
GTindex=GTindex,
file.output=file.output
)
GWAS=myGAPIT$GWAS
GPS=myGAPIT$GPS
REMLs=myGAPIT$REMLs
delta=myGAPIT$ve/myGAPIT$va
vg=myGAPIT$vg
ve=myGAPIT$ve
}
#Performing first screening with Compressed MLM
if(method=="CMLM"){
#print("---------------screening by CMLM----------------------------------")
myGAPIT <- GAPIT(
Y=Y,
CV=CV,
Z=Z,
KI=KI,
GD=GD,
GM=GM,
group.from=1,
group.to=nrow(Y),
QC=FALSE,
GTindex=GTindex,
file.output=file.output
)
GWAS=myGAPIT$GWAS
GPS=myGAPIT$GPS
REMLs=myGAPIT$REMLs
delta=myGAPIT$ve/myGAPIT$va
vg=myGAPIT$vg
ve=myGAPIT$ve
}
#Performing first screening with FaST-LMM
if(method=="FaST" | method=="SUPER"| method=="DC")
{
GWAS=NULL
GPS=NULL
if(!is.null(vg) & !is.null(vg) & is.null(delta)) delta=ve/vg
if(is.null(vg) & is.null(ve))
{
myFaSTREML=GAPIT.get.LL(pheno=matrix(Y[,-1],nrow(Y),1),geno=NULL,snp.pool=as.matrix(GK[,-1]),X0=as.matrix(cbind(matrix(1,nrow(CV),1),CV[,-1])))
#print("Transfer data...")
REMLs=-2*myFaSTREML$LL
delta=myFaSTREML$delta
vg=myFaSTREML$vg
ve=myFaSTREML$ve
#GPS=myFaSTREML$GPS
}
mySUPERFaST=GAPIT.SUPER.FastMLM(ys=matrix(Y[,-1],nrow(Y),1),X0=as.matrix(cbind(matrix(1,nrow(CV),1),CV[,-1])),snp.pool=as.matrix(GK[-1]), xs=as.matrix(GD[GTindex,-1]),vg=vg,delta=delta,LD=LD,method=method)
GWAS=cbind(GM,mySUPERFaST$ps,mySUPERFaST$stats,mySUPERFaST$dfs,mySUPERFaST$effect)
}#End of if(method=="FaST" | method=="SUPER")
if(method=="FARM-CPU")
{
myFarmCPU=FarmCPU(
Y=Y,#Phenotype
GD=GD,#Genotype
GM=GM,#Map information
CV=CV[,2:ncol(CV)],
file.output=TRUE
)
GWAS=myFarmCPU$GWAS
GPS=myFarmCPU$Pred
h2=NULL
vg=NULL
ve=NULL
delta=NULL
REMLs=NULL
}
#print("GAPIT.Bread succeed!")
return (list(GWAS=GWAS, GPS=GPS,REMLs=REMLs,vg=vg,ve=ve,delta=delta))
} #end of GAPIT.Bread
#=============================================================================================
`GAPIT.Burger` <-
function(Y=NULL,CV=NULL,GK=NULL){
#Object: To calculate likelihood, variances and ratio
#Straitegy: NA
#Output: P value
#intput:
#Y: phenotype with columns of taxa,Y1,Y2...
#CV: covariate variables with columns of taxa,v1,v2...
#GK: Genotype data in numerical format, taxa goes to row and snp go to columns. the first column is taxa (same as GAPIT.bread)
#Authors: Xiaolei Liu ,Jiabo Wang and Zhiwu Zhang
#Last update: November 2, 2015
##############################################################################################
#print("GAPIT.Burger in progress...")
if(!is.null(CV)){
#CV=as.matrix(CV)#change CV to a matrix when it is a vector xiaolei changed here
#theCV=as.matrix(cbind(matrix(1,nrow(CV),1),CV)) ###########for FarmCPU
theCV=as.matrix(cbind(matrix(1,nrow(CV),1),CV[,-1])) #reseted by Jiabo ,CV frame is wrong,and not rm taxa
#############for GAPIT other method GWAS
}else{
theCV=matrix(1,nrow(Y),1)
}
#handler of single column GK
n=nrow(GK)
m=ncol(GK)
if(m>2){
theGK=as.matrix(GK[,-1])
}else{
theGK=matrix(GK[,-1],n,1)
}
myFaSTREML=GAPIT.get.LL(pheno=matrix(Y[,-1],nrow(Y),1),geno=NULL,snp.pool=theGK,X0=theCV )
REMLs=-2*myFaSTREML$LL
delta=myFaSTREML$delta
vg=myFaSTREML$vg
ve=myFaSTREML$ve
#print("GAPIT.Burger succeed!")
return (list(REMLs=REMLs,vg=vg,ve=ve,delta=delta))
} #end of GAPIT.Burger.Bus
#=============================================================================================
`GAPIT.CVMergePC` <-
function(X,Y){
#Object: To convert character SNP genotpe to numerical
#Output: Coresponding numerical value
#Authors: Feng Tian and Zhiwu Zhang
# Last update: May 30, 2011
##############################################################################################
#Z=X+Y
Z <- merge(X, Y, by.x = colnames(X)[1], by.y = colnames(Y)[1])
return(Z)
}#end of GAPIT.CVMergePCfunction
#=============================================================================================
`GAPIT.Compress` <-
function(KI,kinship.cluster = "average",kinship.group = "Mean",GN=nrow(KI),Timmer,Memory){
#Object: To cluster individuals into groups based on kinship
#Output: GA, KG
#Authors: Alex Lipka and Zhiwu Zhang
# Last update: April 14, 2011
##############################################################################################
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP start")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp start")
# Extract the line names
line.names <- KI[,1]
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="Does this change memory0")
Memory=GAPIT.Memory(Memory=Memory,Infor="Does this change memory0")
# Remove the first column of the kinship matrix, which is the line names
KI <- KI[ ,-1]
# Convert kinship to distance
#distance.matrix <- 2 - KI
#distance.matrix.as.dist <- as.dist(distance.matrix)
#distance.matrix.as.dist <- as.dist(2 - KI)
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP distance")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp distance")
#print(paste("The value of kinship.cluster is ", kinship.cluster, sep = ""))
# hclust() will perform the hiearchical cluster analysis
#cluster.distance.matrix <- hclust(distance.matrix.as.dist, method = kinship.cluster)
#cluster.distance.matrix <- hclust(as.dist(2 - KI), method = kinship.cluster)
distance.matrix=dist(KI,upper=TRUE) #Jiabo Wang modified ,the dist is right function for cluster
cluster.distance.matrix=hclust(distance.matrix,method=kinship.cluster)
#cutree(out_hclust,k=3)
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP cluster")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp cluster")
# Cutree will assign lines into k clusters
group.membership <- cutree(cluster.distance.matrix, k = GN)
compress_z=table(group.membership,paste(line.names)) #build compress z with group.membership
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP cutree")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp cutree")
#calculate group kinship
if(kinship.group == "Mean"){
#This matrix ooperation is much faster than tapply function for "Mean"
x=as.factor(group.membership)
#b = model.matrix(~x-1)
n=max(as.numeric(as.vector(x)))
b=diag(n)[x,]
KG=t(b)%*%as.matrix(KI)%*%b
CT=t(b)%*%(0*as.matrix(KI)+1)%*%b
KG=as.matrix(KG/CT)
rownames(KG)=c(1:nrow(KG))
colnames(KG)=c(1:ncol(KG))
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP calculation original")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp calculation original")
}else{
gm=as.factor(group.membership)
kv=as.numeric(as.matrix(KI))
kvr=rep(gm,ncol(KI))
kvc=as.numeric(t(matrix(kvr,nrow(KI),ncol(KI))))
kInCol=t(rbind(kv,kvr,kvc))
rm(gm)
rm(kv)
rm(kvr)
rm(kvc)
rm(KI)
gc()
#This part does not work yet
#if(kinship.group == "Mean")
# KG<- tapply(kInCol[,1], list(kInCol[,2], kInCol[,3]), mean)
if(kinship.group == "Max")
KG <- tapply(kInCol[,1], list(kInCol[,2], kInCol[,3]), max)
if(kinship.group == "Min")
KG <- tapply(kInCol[,1], list(kInCol[,2], kInCol[,3]), min)
if(kinship.group == "Median")
KG <- tapply(kInCol[,1], list(kInCol[,2], kInCol[,3]), median)
} #this is end of brancing "Mean" and the rest
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP calculation")
Memory=GAPIT.Memory(Memory=Memory,Infor="cp calculation")
# add line names
#GA <- data.frame(group.membership)
GA <- data.frame(cbind(as.character(line.names),as.numeric(group.membership) ))
#Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="CP Final")
#Memory=GAPIT.Memory(Memory=Memory,Infor="CP Final")
#write.table(KG, paste("KG_from_", kinship.group, "_Method.txt"), quote = FALSE, sep = "\t", row.names = FALSE,col.names = FALSE)
#print("GAPIT.Compress accomplished successfully!")
return(list(GA=GA, KG=KG,Timmer=Timmer,Memory=Memory))
}#The function GAPIT.Compress ends here
#=============================================================================================
`GAPIT.Compression.Visualization` <-
function(Compression = Compression, name.of.trait = name.of.trait){
#Object: Conduct the Benjamini-Hochberg FDR-Controlling Procedure
#Output: Three pdfs: One of the log likelihood function, one of the genetic and error variance component,
# and one of the heritabilities
#Authors: Alex Lipka and Zhiwu Zhang
# Last update: May 10, 2011
##############################################################################################
#Graph the optimum compression
print("GAPIT.Compression.Visualization")
#print(Compression)
if(length(Compression)<=6) Compression=t(as.matrix(Compression[which(Compression[,4]!="NULL" | Compression[,4]!="NaN"),]))
if(length(Compression)==6) Compression=matrix(Compression,1,6)
#print("Compression matrix")
#print(Compression)
#print(length(Compression) )
if(length(Compression)>6) Compression=Compression[which(Compression[,4]!="NULL" | Compression[,4]!="NaN"),]
if(length(Compression)<1) return() #no result
#Pie chart for the optimum setting
#-------------------------------------------------------------------------------
print("Pie chart")
LL=as.numeric(Compression[,4])
Compression.best=Compression[1,]
variance=as.numeric(Compression.best[5:6])
#colors <- c("grey50","grey70")
colors <- c("#990000","dimgray")
varp=variance/sum(variance)
h2.opt= varp[1]
labels0 <- round(varp * 100, 1)
labels <- paste(labels0, "%", sep="")
legend0=c("Genetic: ","Residual: ")
legend <- paste(legend0, round(variance*100)/100, sep="")
LL.best0=as.numeric(Compression.best[4] )
LL.best=paste("-2LL: ",floor(LL.best0*100)/100,sep="")
label.comp=paste(c("Cluster method: ","Group method: ","Group number: "), Compression.best[c(1:3)], sep="")
theOptimum=c(label.comp,LL.best)
pdf(paste("GAPIT.", name.of.trait,".Optimum.pdf", sep = ""), width = 14)
par(mfrow = c(1,1), mar = c(1,1,5,5), lab = c(5,5,7))
pie(variance, col=colors, labels=labels,angle=45,border=NA)
legend(1.0, 0.5, legend, cex=1.5, bty="n",
fill=colors)
#Display the optimum compression
text(1.5,.0, "The optimum compression", col= "gray10")
for(i in 1:4){
text(1.5,-.1*i, theOptimum[i], col= "gray10")
}
dev.off()
#sort Compression by group number for plot order
Compression=Compression[order(as.numeric(Compression[,3])),]
#Graph compression with multiple groups
#print("Graph compression with multiple groups")
if(length(Compression)==6) return() #For to exit if only one row
#print("It should not go here")
if(length(unique(Compression[,3]))>1)
{
#Create a vector of colors
#print("Setting colors")
color.vector.basic <- c("red","blue","black", "blueviolet","indianred","cadetblue","orange")
color.vector.addition <- setdiff(c(colors()[grep("red",colors())], colors()[grep("blue",colors())]),color.vector.basic )
color.vector.addition.mixed <- sample(color.vector.addition,max(0,((length(unique(Compression[,1])) * length(unique(Compression[,2])))-length(color.vector.basic))))
color.vector <- c(color.vector.basic,color.vector.addition.mixed )
#Create a vector of numbers for the line dot types
line.vector <- rep(1:(length(unique(Compression[,1])) * length(unique(Compression[,2]))))
#We want to have a total of three plots, one displaying the likelihood function, one displaying the variance components, and one displaying the
# heritability
pdf(paste("GAPIT.", name.of.trait,".Compression.multiple.group.", ".pdf", sep = ""), width = 14)
par(mfrow = c(2,3), mar = c(5,5,1,1), lab = c(5,5,7))
# Make the likelihood function plot
#print("Likelihood")
k <- 1
for(i in 1:length(unique(Compression[,1]))){
for(j in 1:length(unique(Compression[,2]))){
if((i == 1)&(j == 1)) {
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,4])
plot(y~x,type="l", pch = 30, lty = line.vector[i], ylim=c(min(as.numeric(Compression[,4])),max(as.numeric(Compression[,4]))), xlim = c(min(as.numeric(Compression[,3])),max(as.numeric(Compression[,3]))),
col = color.vector[j], xlab = "Number of Groups", ylab = "-2Log Likelihoood",lwd=1 )
label = paste(c(as.character(unique(Compression[,1]))[k]," ",as.character(unique(Compression[,2]))[j]), collapse = "")
}
if((i != 1)|(j != 1)) {
k <- k+1
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,4])
lines(y~x,type="l", pch = 30, lty = line.vector[i], col = color.vector[j])
label = c(label, paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = ""))
}
}
}
#Make a legend
#legend("topright", label, fill = color.vector)
legend.col= 1+floor(length(unique(Compression[,1])) * length(unique(Compression[,2]))/20)
line.style=rep(1:length(unique(Compression[,1])), each = length(unique(Compression[,2])))
line.color=rep(1:length(unique(Compression[,2])), length(unique(Compression[,1])))
legend("topright", label, col = color.vector[line.color], lty = line.style, ncol=legend.col,horiz=FALSE,bty="n")
# Make the genetic variance component plots
#print("genetic variance")
k <- 1
for(i in 1:length(unique(Compression[,1]))){
for(j in 1:length(unique(Compression[,2]))){
if((i == 1)&(j == 1)) {
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,5])
plot(y~x,type="l", pch = 17, lty = line.vector[i], ylim=c(min(as.numeric(Compression[,5])),max(as.numeric(Compression[,5]))), xlim = c(min(as.numeric(Compression[,3])),max(as.numeric(Compression[,3]))),
col = color.vector[j], xlab = "Number of Groups", ylab = "Genetic Variance", )
#label = paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = "")
}
if((i != 1)|(j != 1)) {
k <- k+1
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,5])
lines(y~x,type="l", pch = 17, lty = line.vector[i], col = color.vector[j])
#label = c(label, paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = ""))
}
}
}
#Make a legend
#legend("topleft", label, fill = color.vector)
# Make the residual variance component plots
k <- 1
for(i in 1:length(unique(Compression[,1]))){
for(j in 1:length(unique(Compression[,2]))){
if((i == 1)&(j == 1)) {
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,6])
plot(y~x,type="l", pch = 17, ylim=c(min(as.numeric(Compression[,6])),max(as.numeric(Compression[,6]))), xlim = c(min(as.numeric(Compression[,3])),max(as.numeric(Compression[,3]))),
col = color.vector[j], xlab = "Number of Groups", ylab = "Residual Variance", )
#label = paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = "")
}
if((i != 1)|(j != 1)) {
k <- k+1
Compression.subset <- Compression[which( (Compression[,1] == as.character(unique(Compression[,1])[i])) & (Compression[,2] == as.character(unique(Compression[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,6])
lines(y~x,type="l", pch = 17, lty = line.vector[i], col = color.vector[j])
#label = c(label, paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = ""))
}
}
}
#Make a legend
#legend("topright", label, fill = color.vector)
#calculate total variance and h2
#print("h2")
heritablilty.vector <- as.numeric(Compression[,5])/(as.numeric(Compression[,5]) + as.numeric(Compression[,6]))
totalVariance.vector <- as.numeric(as.numeric(Compression[,5]) + as.numeric(Compression[,6]))
Compression.h2 <- cbind(Compression, heritablilty.vector,totalVariance.vector)
# Make the total variance component plots
#print("Total variance")
k <- 1
for(i in 1:length(unique(Compression.h2[,1]))){
for(j in 1:length(unique(Compression.h2[,2]))){
if((i == 1)&(j == 1)) {
Compression.subset <- Compression.h2[which( (Compression.h2[,1] == as.character(unique(Compression.h2[,1])[i])) & (Compression.h2[,2] == as.character(unique(Compression.h2[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,8])
plot(y~x,type="l", pch = 17, lty = line.vector[k], ylim=c(min(as.numeric(Compression.h2[,8])),max(as.numeric(Compression.h2[,8]))), xlim = c(min(as.numeric(Compression.h2[,3])),max(as.numeric(Compression.h2[,3]))),
col = color.vector[1], xlab = "Number of Groups", ylab = "Total Variance", )
#label = paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = "")
}
if((i != 1)|(j != 1)) {
k <- k+1
Compression.subset <- Compression.h2[which( (Compression.h2[,1] == as.character(unique(Compression.h2[,1])[i])) & (Compression.h2[,2] == as.character(unique(Compression.h2[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,8])
lines(y~x,type="l", pch = 17, lty = line.vector[i], col = color.vector[j])
#label = c(label, paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = ""))
}
}
}
#Make a legend
#legend("topright", label, fill = color.vector)
# Make the heritability plots
#print("h2 plot")
k <- 1
for(i in 1:length(unique(Compression[,1]))){
for(j in 1:length(unique(Compression[,2]))){
if((i == 1)&(j == 1)) {
Compression.subset <- Compression.h2[which( (Compression.h2[,1] == as.character(unique(Compression.h2[,1])[i])) & (Compression.h2[,2] == as.character(unique(Compression.h2[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,7])
plot(y~x,type="l", pch = 17, lty = line.vector[k], ylim=c(min(as.numeric(Compression.h2[,7])),max(as.numeric(Compression.h2[,7]))), xlim = c(min(as.numeric(Compression.h2[,3])),max(as.numeric(Compression.h2[,3]))),
col = color.vector[1], xlab = "Number of Groups", ylab = "Heritability", )
#label = paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = "")
}
if((i != 1)|(j != 1)) {
k <- k+1
Compression.subset <- Compression.h2[which( (Compression.h2[,1] == as.character(unique(Compression.h2[,1])[i])) & (Compression.h2[,2] == as.character(unique(Compression.h2[,2])[j])) ), ]
x <- as.numeric(Compression.subset[,3])
y <- as.numeric(Compression.subset[,7])
lines(y~x,type="l", lty = line.vector[i], pch = 17, col = color.vector[j])
#label = c(label, paste(c(as.character(unique(Compression[,1]))[i]," ",as.character(unique(Compression[,2]))[j]), collapse = ""))
}
}
}
#Make a legend
#legend("topleft", label, fill = color.vector)
legend.col= 1+floor(length(unique(Compression[,1])) * length(unique(Compression[,2]))/20)
line.style=rep(1:length(unique(Compression[,1])), each = length(unique(Compression[,2])))
line.color=rep(1:length(unique(Compression[,2])), length(unique(Compression[,1])))
# Make labels
plot(0~0,axes=FALSE,type="l",ylab = "",xlab = "",frame.plot=FALSE)
legend("topleft", label, col = color.vector[line.color], lty = line.style, ncol=legend.col,horiz=FALSE)
dev.off()
}#end of Graph compression with multiple groups
#Graph compression with single groups
#print("Graph compression with single groups")
if(length(unique(Compression[,3]))==1& length(unique(Compression[,1]))*length(unique(Compression[,2]))>1)
{
#Graph the compression with only one group
pdf(paste("GAPIT.Compression.single.group.", name.of.trait, ".pdf", sep = ""), width = 14)
par(mfrow = c(2,2), mar = c(5,5,1,1), lab = c(5,5,7))
nkt=length(unique(Compression[,1]))
nca=length(unique(Compression[,2]))
kvr=rep(c(1:nkt),nca)
kvc0=rep(c(1:nca),nkt)
kvc=as.numeric(t(matrix(kvc0,nca,nkt)))
kt.name=Compression[1:nkt,1]
ca.index=((1:nca)-1)*nkt+1
ca.name=Compression[ca.index,2]
KG<- t(tapply(as.numeric(Compression[,4]), list(kvr, kvc), mean))
colnames(KG)=kt.name
barplot(as.matrix(KG), ylab= "-2 Log Likelihood",beside=TRUE, col=rainbow(length(unique(Compression[,2]))))
KG<- t(tapply(as.numeric(Compression[,5]), list(kvr, kvc), mean))
colnames(KG)=kt.name
barplot(as.matrix(KG), ylab= "Genetic varaince", beside=TRUE, col=rainbow(length(unique(Compression[,2]))))
KG<- t(tapply(as.numeric(Compression[,6]), list(kvr, kvc), mean))
colnames(KG)=kt.name
barplot(as.matrix(KG), ylab= "Residual varaince", beside=TRUE, col=rainbow(length(unique(Compression[,2]))))
KG<- t(tapply(as.numeric(Compression[,5])/(as.numeric(Compression[,5])+as.numeric(Compression[,6])), list(kvr, kvc), mean))
colnames(KG)=kt.name
barplot(as.matrix(KG), ylab= "Heritability", beside=TRUE, col=rainbow(length(unique(Compression[,2]))),ylim=c(0,1))
legend("topleft", paste(t(ca.name)), cex=0.8,bty="n", fill=rainbow(length(unique(Compression[,2]))),horiz=TRUE)
dev.off()
} #end of Graph compression with single groups
print("GAPIT.Compression.Visualization accomplished successfully!")
#return(list(compression=Compression.h2,h2=h2.opt))
return
}#GAPIT.Compression.Plots ends here
#=============================================================================================
`GAPIT.Create.Indicator` <-
function(xs, SNP.impute = "Major" ){
#Object: To esimate variance component by using EMMA algorithm and perform GWAS with P3D/EMMAx
#Output: ps, REMLs, stats, dfs, vgs, ves, BLUP, BLUP_Plus_Mean, PEV
#Authors: Alex Lipka and Zhiwu Zhang
# Last update: April 30, 2012
##############################################################################################
#Determine the number of bits of the genotype
bit=nchar(as.character(xs[1]))
#Identify the SNPs classified as missing
if(bit==1) {
xss[xss=="xs"]="N"
xs[xs=="-"]="N"
xs[xs=="+"]="N"
xs[xs=="/"]="N"
xs[xs=="K"]="Z" #K (for GT genotype)is is replaced by Z to ensure heterozygose has the largest value
}
if(bit==2) {
xs[xs=="xsxs"]="N"
xs[xs=="--"]="N"
xs[xs=="++"]="N"
xs[xs=="//"]="N"
xs[xs=="NN"]="N"
}
#Create the indicators
#Sort the SNPs by genotype frequency
xs.temp <- xs[-which(xs == "N")]
frequ<- NULL
for(i in 1:length(unique(xs.temp))) frequ <- c(frequ, length(which(xs == unique(xs)[i])))
unique.sorted <- cbind(unique(xs.temp), frequ)
print("unique.sorted is")
print(unique.sorted)
unique.sorted <- unique.sorted[order(unique.sorted[,2]),]
unique.sorted <- unique.sorted[,-2]
#Impute based on the major and minor allele frequencies
if(SNP.impute == "Major") xs[which(is.na(xs))] = unique.sorted[1]
if(SNP.impute == "Minor") xs[which(is.na(xs))] = unique.sorted[length(unique.sorted)]
if(SNP.impute == "Middle") xs[which(is.na(xs))] = unique.sorted[2]
x.ind <- NULL
for(i in unique.sorted){
x.col <- rep(NA, length(xs))
x.col[which(xs==i)] <- 1
x.col[which(xs!=i)] <- 0
x.ind <- cbind(x.ind,x.col)
}
return(x.ind)
print("GAPIT.Create.Indicator accomplished successfully!")
}#end of GAPIT.Create.Indicator function
#=============================================================================================
`GAPIT.EMMAxP3D` <-
function(ys,xs,K=NULL,Z=NULL,X0=NULL,CVI=NULL,CV.Inheritance=NULL,GI=NULL,GP=NULL,
file.path=NULL,file.from=NULL,file.to=NULL,file.total=1, genoFormat="Hapmap", file.fragment=NULL,byFile=FALSE,fullGD=TRUE,SNP.fraction=1,
file.G=NULL,file.Ext.G=NULL,GTindex=NULL,file.GD=NULL, file.GM=NULL, file.Ext.GD=NULL,file.Ext.GM=NULL,
SNP.P3D=TRUE,Timmer,Memory,optOnly=TRUE,SNP.effect="Add",SNP.impute="Middle", SNP.permutation=FALSE,
ngrids=100,llim=-10,ulim=10,esp=1e-10,name.of.trait=NULL, Create.indicator = FALSE, Major.allele.zero = FALSE){
#Object: To esimate variance component by using EMMA algorithm and perform GWAS with P3D/EMMAx
#Output: ps, REMLs, stats, dfs, vgs, ves, BLUP, BLUP_Plus_Mean, PEV
#Authors: Feng Tian, Alex Lipka and Zhiwu Zhang
# Last update: April 6, 2016
# Library used: EMMA (Kang et al, Genetics, Vol. 178, 1709-1723, March 2008)
# Note: This function was modified from the function of emma.REML.t from the library
##############################################################################################
#print("EMMAxP3D started...")
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="P3D Start")
Memory=GAPIT.Memory(Memory=Memory,Infor="P3D Start")
#When numeric genotypes are selected, impute the missing SNPs with the allele indicated by the "SNP.impute" value
if(!optOnly){
if(SNP.impute == "Major") xs[which(is.na(xs))] = 2
if(SNP.impute == "Minor") xs[which(is.na(xs))] = 0
if(SNP.impute == "Middle") xs[which(is.na(xs))] = 1
}
#--------------------------------------------------------------------------------------------------------------------<
#Change data to matrix format if they are not
if(is.null(dim(ys)) || ncol(ys) == 1) ys <- matrix(ys, 1, length(ys))
if(is.null(X0)) X0 <- matrix(1, ncol(ys), 1)
#handler of special Z and K
if(!is.null(Z)){ if(ncol(Z) == nrow(Z)) Z = NULL }
if(!is.null(K)) {if(length(K)<= 1) K = NULL}
#Extract dimension information
g <- nrow(ys) #number of traits
n <- ncol(ys) #number of observation
q0 <- ncol(X0)#number of fixed effects
q1 <- q0 + 1 #Nuber of fixed effect including SNP
nr=n
if(!is.null(K)) tv=ncol(K)
#decomposation without fixed effect
#print("Caling emma.eigen.L...")
if(!is.null(K)) eig.L <- emma.eigen.L(Z, K) #this function handle both NULL Z and non-NULL Z matrix
#eig.L$values[eig.L$values<0]=0
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="eig.L")
Memory=GAPIT.Memory(Memory=Memory,Infor="eig.L")
#decomposation with fixed effect (SNP not included)
#print("Calling emma.eigen.R.w.Z...")
X <- X0 #covariate variables such as population structure
if(!is.null(Z) & !is.null(K)) eig.R <- try(emma.eigen.R.w.Z(Z, K, X),silent=TRUE) #This will be used to get REstricted ML (REML)
if(is.null(Z) & !is.null(K)) eig.R <- try(emma.eigen.R.wo.Z( K, X),silent=TRUE) #This will be used to get REstricted ML (REML)
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="eig.R")
Memory=GAPIT.Memory(Memory=Memory,Infor="eig.R")
#eig.R$values[eig.R$values<0]=0
#print(labels(eig.R))
#print(length(eig.R$values))
#print(dim(eig.R$vectors))
#print("emma.eigen.R.w.Z called!!!")
#Handler of error in emma
if(!is.null(K)){
if(inherits(eig.R, "try-error"))
return(list(ps = NULL, REMLs = NA, stats = NULL, effect.est = NULL, dfs = NULL,maf=NULL,nobs = NULL,Timmer=Timmer,Memory=Memory,
vgs = NA, ves = NA, BLUP = NULL, BLUP_Plus_Mean = NULL,
PEV = NULL, BLUE=NULL))
}
#-------------------------------------------------------------------------------------------------------------------->
#print("Looping through traits...")
#Loop on Traits
for (j in 1:g)
{
if(optOnly){
#REMLE <- GAPIT.emma.REMLE(ys[j,], X, K, Z, ngrids, llim, ulim, esp, eig.R)
#vgs <- REMLE$vg
#ves <- REMLE$ve
#REMLs <- REMLE$REML
#REMLE_delta=REMLE$delta
if(!is.null(K)){
REMLE <- GAPIT.emma.REMLE(ys[j,], X, K, Z, ngrids, llim, ulim, esp, eig.R)
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="REML")
Memory=GAPIT.Memory(Memory=Memory,Infor="REML")
rm(eig.R)
gc()
Timmer=GAPIT.Timmer(Timmer=Timmer,Infor="eig.R removed")
Memory=GAPIT.Memory(Memory=Memory,Infor="eig.R removed")
vgs <- REMLE$vg
ves <- REMLE$ve
REMLs <- REMLE$REML
REMLE_delta=REMLE$delta
rm(REMLE)
gc()
}
vids <- !is.na(ys[j,])
yv <- ys[j, vids]
if(!is.null(Z) & !is.null(K)) U <- eig.L$vectors * matrix(c(sqrt(1/(eig.L$values + REMLE_delta)),rep(sqrt(1/REMLE_delta),nr - tv)),nr,((nr-tv)+length(eig.L$values)),byrow=TRUE)
if( is.null(Z) & !is.null(K)) U <- eig.L$vectors * matrix( sqrt(1/(eig.L$values + REMLE_delta)),nr,length(eig.L$values),byrow=TRUE)
if( !is.null(Z) & !is.null(K)) eig.full.plus.delta <- as.matrix(c((eig.L$values + REMLE_delta), rep(REMLE_delta,(nr - tv))))
if( is.null(Z) & !is.null(K)) eig.full.plus.delta <- as.matrix((eig.L$values + REMLE_delta))
if(!is.null(K)){
if(length(which(eig.L$values < 0)) > 0 ){
#print("---------------------------------------------------The group kinship matrix at this compression level is not positive semidefinite. Please select another compression level.---------------------------------------------------")
#return(list(ps = NULL, REMLs = 999999, stats = NULL, effect.est = NULL, dfs = NULL,maf=NULL,nobs = NULL,Timmer=Timmer,Memory=Memory,
#vgs = 1.000, ves = 1.000, BLUP = NULL, BLUP_Plus_Mean = NULL,
#PEV = NULL, BLUE=NULL))
}
}
#Calculate the log likelihood function for the intercept only model
X.int <- matrix(1,nrow(as.matrix(yv)),ncol(as.matrix(yv)))
iX.intX.int <- solve(crossprod(X.int, X.int))
iX.intY <- crossprod(X.int, as.matrix(as.matrix(yv)))
beta.int <- crossprod(iX.intX.int, iX.intY) #Note: we can use crossprod here becase iXX is symmetric
X.int.beta.int <- X.int%*%beta.int
logL0 <- 0.5*((-length(yv))*log(((2*pi)/length(yv))
*crossprod((yv-X.int.beta.int),(yv-X.int.beta.int)))
-length(yv))
#print(paste("The value of logL0 inside of the optonly template is is",logL0, sep = ""))
#print(paste("The value of nrow(as.matrix(ys[!is.na(ys)])) is ",nrow(as.matrix(ys[!is.na(ys)])), sep = ""))
if(!is.null(K)){
yt <- yt <- crossprod(U, yv)
X0t <- crossprod(U, X0)
X0X0 <- crossprod(X0t, X0t)
X0Y <- crossprod(X0t,yt)
XY <- X0Y
iX0X0 <- try(solve(X0X0),silent=TRUE)
if(inherits(iX0X0, "try-error")){
iX0X0 <- ginv(X0X0)
print("At least two of your covariates are linearly dependent. Please reconsider the covariates you are using for GWAS and GPS")
}
iXX <- iX0X0
}