#4. GTA K Pairwise Compare 
#Sean's Arg convention for info only
#use this Rscript to compare two results sheets from GTF analysis "Average_GOTerms_All.csv"
#Arg1 is Average_GOTerms_All_1.csv
#Arg2 is the name to give GTA results 1
#Arg3 is Average_GOTerms_All2.csv
#Arg4 is the name to give GTA results 2
#Arg5 is the directory to put the files into 
#Arg 1 is the GTA results 1
#Arg 2 is the name of GTA results 1 to print in the results
#Arg 3 is GTA results 3
#Arg 4 is the name of GTA results 2 to print in the results
#Arg 4 is the name of GTA results 2 to print in the results
#arg 5 is the directory to put the results into (and create that directory if needed)

library(ggplot2)
library(plotly)
library(htmlwidgets)
library(extrafont)
library(grid)
library(ggthemes)
Args <- commandArgs(TRUE)
expNm= Args[1]  #"Exp3"
expNm[2]= Args[2] #"Exp4"

expNumber1<- as.numeric(sub("^.*?(\\d+)$", "\\1", expNm[1]))
expNumber2<- as.numeric(sub("^.*?(\\d+)$", "\\1", expNm[2]))
#labels <- read.delim("ExpLabels.txt",skip=0,as.is=T,row.names=1,strip.white=TRUE)
labels <- read.delim("StudyInfo.txt",skip=0,as.is=T,row.names=1,strip.white=TRUE)
Name1 <- labels[expNumber1,1] #ssArg2 These are now supplied by Code/ExpLabels.txt which is user edited
Name2 <- labels[expNumber2,1] #ssArg4
Wstudy= getwd()
input_file1 <- paste0("../GTAresults/",expNm[1],"/Average_GOTerms_All.csv" )  #Args[1]
input_file2 <-  paste0("../GTAresults/",expNm[2],"/Average_GOTerms_All.csv" )  #Args[3]

pairDirL= paste0("../GTAresults/","PairwiseCompareL_",expNm[1],"-",expNm[2])
pairDirK= paste0("../GTAresults/","PairwiseCompareK_",expNm[1],"-",expNm[2])
outPathGTAcompare= "../GTAresults/PairwiseCompareL" #paste0(Wstudy,"/GTAresults/PairwiseCompareL)
outPathGTAcompare[2]= "../GTAresults/PairwiseCompareK"  #paste0(Wstudy,"/GTAresults/PairwiseCompareK")
#dir.create(outPathGTAcompare[1])
dir.create(pairDirL)  #(outPathGTAcompare[1])
dir.create(pairDirK)  #(outPathGTAcompare[2])


#define the output path (as fourth argument from Rscript)
outputpath <- pairDirK #outPathGTAcompare[2]   #Args[5]

#theme elements for plots
theme_Publication <- function(base_size=14, base_family="sans") {
  (theme_foundation(base_size=base_size, base_family=base_family)
   + theme(plot.title = element_text(face = "bold",
                                     size = rel(1.2), hjust = 0.5),
           text = element_text(),
           panel.background = element_rect(colour = NA),
           plot.background = element_rect(colour = NA),
           panel.border = element_rect(colour = NA),
           axis.title = element_text(face = "bold",size = rel(1)),
           axis.title.y = element_text(angle=90,vjust =2),
           axis.title.x = element_text(vjust = -0.2),
           axis.text = element_text(), 
           axis.line = element_line(colour="black"),
           axis.ticks = element_line(),
           panel.grid.major = element_line(colour="#f0f0f0"),
           panel.grid.minor = element_blank(),
           legend.key = element_rect(colour = NA),
           legend.position = "bottom",
           legend.direction = "horizontal",
           legend.key.size= unit(0.2, "cm"),
           legend.spacing = unit(0, "cm"),
           legend.title = element_text(face="italic"),
           plot.margin=unit(c(10,5,5,5),"mm"),
           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
           strip.text = element_text(face="bold")
   ))
}

scale_fill_Publication <- function(...){
  library(scales)
  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}
scale_colour_Publication <- function(...){
  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}

theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
  (theme_foundation(base_size=base_size, base_family=base_family)
   + theme(plot.title = element_text(face = "bold",
                                     size = rel(1.2), hjust = 0.5),
           text = element_text(),
           panel.background = element_rect(colour = NA),
           plot.background = element_rect(colour = NA),
           panel.border = element_rect(colour = NA),
           axis.title = element_text(face = "bold",size = rel(1)),
           axis.title.y = element_text(angle=90,vjust =2),
           axis.title.x = element_text(vjust = -0.2),
           axis.text = element_text(), 
           axis.line = element_line(colour="black"),
           axis.ticks = element_line(),
           panel.grid.major = element_line(colour="#f0f0f0"),
           panel.grid.minor = element_blank(),
           legend.key = element_rect(colour = NA),
           legend.position = "right",
           legend.direction = "vertical",
           legend.key.size= unit(0.5, "cm"),
           legend.spacing = unit(0, "cm"),
           legend.title = element_text(face="italic"),
           plot.margin=unit(c(10,5,5,5),"mm"),
           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
           strip.text = element_text(face="bold")
   ))
}

scale_fill_Publication <- function(...){
  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}
scale_colour_Publication <- function(...){
  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}

X1 <- read.csv(file = input_file1,stringsAsFactors=FALSE,header = TRUE)

X2 <- read.csv(file = input_file2,stringsAsFactors=FALSE,header = TRUE)

#1
X <- merge(X1,X2,by ="Term_Avg",all=TRUE,suffixes = c("_X1","_X2"))

gg <- ggplot(data = X,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Ontology_Avg_X1,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep = "")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOntology.pdf",sep=""),width = 12,height = 8)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("127")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOntology.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#2
#ID aggravators and alleviators, regardless of whether they meet 2SD threshold
X1_Specific_Aggravators <- X[which(X$Z_lm_K_Avg_X1 >= 2 & X$Z_lm_K_Avg_X2 < 2),]
X1_Specific_Alleviators <- X[which(X$Z_lm_K_Avg_X1 <= -2 & X$Z_lm_K_Avg_X2 > -2),]

X2_Specific_Aggravators <- X[which(X$Z_lm_K_Avg_X2 >= 2 & X$Z_lm_K_Avg_X1 < 2),]
X2_Specific_Alleviators <- X[which(X$Z_lm_K_Avg_X2 <= -2 & X$Z_lm_K_Avg_X1 > -2),]

Overlap_Aggravators <- X[which(X$Z_lm_K_Avg_X1 >= 2 & X$Z_lm_K_Avg_X2 >= 2),]
Overlap_Alleviators <- X[which(X$Z_lm_K_Avg_X1 <= -2 & X$Z_lm_K_Avg_X2 <= -2),]

X2_Specific_Aggravators_X1_Alleviatiors <- X[which(X$Z_lm_K_Avg_X2 >= 2 & X$Z_lm_K_Avg_X1 <= -2),]
X2_Specific_Alleviators_X1_Aggravators <- X[which(X$Z_lm_K_Avg_X2 <= -2 & X$Z_lm_K_Avg_X1 >= 2),]

X$Overlap_Avg <- NA

try(X[X$Term_Avg %in% X1_Specific_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name1,"Specific_Deletion_Suppressors",sep="_"))
try(X[X$Term_Avg %in% X1_Specific_Alleviators$Term_Avg,]$Overlap_Avg <- paste(Name1,"Specific_Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Specific_Deletion_Suppressors",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Alleviators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Specific_Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% Overlap_Aggravators$Term_Avg,]$Overlap_Avg <- "Overlapping_Deletion_Suppressors")
try(X[X$Term_Avg %in% Overlap_Alleviators$Term_Avg,]$Overlap_Avg <- "Overlapping_Deletion_Enhancers")
try(X[X$Term_Avg %in% X2_Specific_Aggravators_X1_Alleviatiors$Term_Avg,]$Overlap_Avg <- paste(Name2,"Deletion_Suppressors",Name1,"Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Alleviators_X1_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Deletion_Enhancers",Name1,"Deletion_Suppressors",sep="_"))


plotly_path <- paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_byOverlap.html",sep="")
gg <- ggplot(data = X,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap.pdf",sep=""),width = 12,height = 8)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#2-170")
#2
fname <- paste("/Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOverlap.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#3
x_rem2_gene <- X[X$NumGenes_Avg_X1 >= 2 & X$NumGenes_Avg_X2 >= 2,]
plotly_path <- paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_byOverlap_above2genes.html",sep="")
gg <- ggplot(data = x_rem2_gene,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_above2genes.pdf",sep=""),width = 12,height = 8)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#3")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOverlap_above2genes.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#4
X_overlap_nothresold <- X[!(is.na(X$Overlap_Avg)),]
gg <- ggplot(data = X_overlap_nothresold,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep = "")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_Above2SD_ByOverlap.pdf",sep=""),width = 12,height = 8)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#4")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_Above2SD_ByOverlap.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#5
#only output GTA terms where average score is still above 2 after subtracting the SD
#Z1 will ID aggravators, Z2 alleviators
Z1 <- X
Z1$L_Subtract_SD_X1 <- Z1$Z_lm_K_Avg_X1 - Z1$Z_lm_K_SD_X1
Z1$L_Subtract_SD_X2 <- Z1$Z_lm_K_Avg_X2 - Z1$Z_lm_K_SD_X2

Z2 <- X
Z2$L_Subtract_SD_X1 <- Z1$Z_lm_K_Avg_X1 + Z1$Z_lm_K_SD_X1
Z2$L_Subtract_SD_X2 <- Z1$Z_lm_K_Avg_X2 + Z1$Z_lm_K_SD_X2


X1_Specific_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X1 >= 2 & Z1$L_Subtract_SD_X2 < 2),]
X1_Specific_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X1 <= -2 & Z2$L_Subtract_SD_X2 > -2),]

X2_Specific_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X2 >= 2 & Z1$L_Subtract_SD_X1 < 2),]
X2_Specific_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z2$L_Subtract_SD_X1 > -2),]

Overlap_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X1 >= 2 & Z1$L_Subtract_SD_X2 >= 2),]
Overlap_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z2$L_Subtract_SD_X1 <= -2),]

X2_Specific_Aggravators2_X1_Alleviatiors2 <- Z1[which(Z1$L_Subtract_SD_X2 >= 2 & Z2$L_Subtract_SD_X1 <= -2),]
X2_Specific_Alleviators2_X1_Aggravators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z1$L_Subtract_SD_X1 >= 2),]

X$Overlap <- NA

try(X[X$Term_Avg %in% X1_Specific_Aggravators2$Term_Avg,]$Overlap <- paste(Name1,"Specific_Deletion_Suppressors",sep="_"))
try(X[X$Term_Avg %in% X1_Specific_Alleviators2$Term_Avg,]$Overlap <- paste(Name1,"Specific_Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Aggravators2$Term_Avg,]$Overlap <- paste(Name2,"Specific_Deletion_Suppressors",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Alleviators2$Term_Avg,]$Overlap <- paste(Name2,"Specific_Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% Overlap_Aggravators2$Term_Avg,]$Overlap <- "Overlapping_Deletion_Suppressors")
try(X[X$Term_Avg %in% Overlap_Alleviators2$Term_Avg,]$Overlap <- "Overlapping_Deletion_Enhancers")
try(X[X$Term_Avg %in% X2_Specific_Aggravators2_X1_Alleviatiors2$Term_Avg,]$Overlap <- paste(Name2,"Deletion_Suppressors",Name1,"Deletion_Enhancers",sep="_"))
try(X[X$Term_Avg %in% X2_Specific_Alleviators2_X1_Aggravators2$Term_Avg,]$Overlap <- paste(Name2,"Deletion_Enhancers",Name1,"Deletion_Suppressors",sep="_"))

X_abovethreshold <- X[!(is.na(X$Overlap)),]
gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold.pdf",sep=""),width = 12,height = 8)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#5")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#6
gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_text(aes(label=Term_Avg),nudge_y = 0.25,size=2) + 
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3,size=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_names.pdf",sep=""),width = 20,height = 20)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#6")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_names.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#7
X_abovethreshold$X1_Rank <- NA
X_abovethreshold$X1_Rank <- rank(-X_abovethreshold$Z_lm_K_Avg_X1,ties.method = "random")
X_abovethreshold$X2_Rank <- NA
X_abovethreshold$X2_Rank <- rank(-X_abovethreshold$Z_lm_K_Avg_X2,ties.method = "random")


gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_text(aes(label=X1_Rank),nudge_y = 0.25,size=4) + 
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3,size=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX1.pdf",sep=""),width = 15,height = 15)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#7")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX1.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

#8
gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_K_Avg_X1,y=Z_lm_K_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_K_SD_X1,SD_2=Z_lm_K_SD_X2)) + 
  xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
  geom_text(aes(label=X2_Rank),nudge_y = 0.25,size=4) + 
  geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3,size=3) +
  ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
  theme_Publication_legend_right()
pdf(paste(getwd(),"/",outputpath,"/","Scatter_lm_GTF_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX2.pdf",sep=""),width = 15,height = 15)
gg
dev.off()
pgg <- ggplotly(gg)
#pgg
print("#8")
fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX2.html",sep="")
print(fname)
htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
file.rename(from = file.path(getwd(),fname), to =  file.path(pairDirK,fname))

print("write csv files")
write.csv(x=X,file = paste(getwd(),"/",outputpath,"/","All_GTF_Avg_Scores_",Name1,"_vs_",Name2,".csv",sep=""),row.names = FALSE)
write.csv(x=X_abovethreshold,file = paste(getwd(),"/",outputpath,"/","AboveThreshold_GTF_Avg_Scores_",Name1,"_vs_",Name2,".csv",sep=""),row.names = FALSE)

#End of GTA Pairwise compare for K values