333 lines
19 KiB
R
333 lines
19 KiB
R
#4.This R script performs GTA L Pairwise Compares for user specified pairs of Experiments
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#Arg1 is the first Experiment or a user ordered pair (i.e., Exp3)
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#Arg2 is the second Experiment or a user ordered pair (i.e., Exp4)
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#Note user can put whatever names they wish for the Canonical Exp1..4 in the ../Code/ExpLabels.txt spread sheet
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#which correlates the canonical Exp Name with the user stylized label for plots and output files.
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library(ggplot2)
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library(plotly)
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library(htmlwidgets)
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library(extrafont)
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library(grid)
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library(ggthemes)
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Args <- commandArgs(TRUE)
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expNm= Args[1] #i.e., "Exp3"
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expNm[2]= Args[2] #i.e., "Exp4"
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expNumber1<- as.numeric(sub("^.*?(\\d+)$", "\\1", expNm[1]))
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expNumber2<- as.numeric(sub("^.*?(\\d+)$", "\\1", expNm[2]))
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#labels <- read.delim("ExpLabels.txt",skip=0,as.is=T,row.names=1,strip.white=TRUE)
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labels <- read.delim("StudyInfo.txt",skip=0,as.is=T,row.names=1,strip.white=TRUE)
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Name1 <- labels[expNumber1,1] #ssArg2 These are now supplied by Code/ExpLabels.txt which is user edited
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Name2 <- labels[expNumber2,1] #ssArg4
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Wstudy= getwd()
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input_file1 <- paste0("../GTAresults/",expNm[1],"/Average_GOTerms_All.csv" ) #Args[1]
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input_file2 <- paste0("../GTAresults/",expNm[2],"/Average_GOTerms_All.csv" ) #Args[3]
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pairDirL= paste0("../GTAresults/","PairwiseCompareL_",expNm[1],"-",expNm[2])
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pairDirK= paste0("../GTAresults/","PairwiseCompareK_",expNm[1],"-",expNm[2])
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outPathGTAcompare= "../GTAresults/PairwiseCompareL" #paste0(Wstudy,"/GTAresults/PairwiseCompareL)
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outPathGTAcompare[2]= "../GTAresults/PairwiseCompareK" #paste0(Wstudy,"/GTAresults/PairwiseCompareK")
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#dir.create(outPathGTAcompare[1])
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dir.create(pairDirL) #(outPathGTAcompare[1])
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dir.create(pairDirK) #(outPathGTAcompare[2])
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###########BEGIN PAIRWISE L-----LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL
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#define the output path (as fourth argument from Rscript)
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outputpath <- pairDirL #outPathGTAcompare[1] #Args[5]
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#outputPlotly <- "../GTAresults/PairwiseCompareL/" #"/GTAresults/PairwiseCompareL/"
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print("39")
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#theme elements for plots
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theme_Publication <- function(base_size=14, base_family="sans") {
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(theme_foundation(base_size=base_size, base_family=base_family)
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+ theme(plot.title = element_text(face = "bold",
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size = rel(1.2), hjust = 0.5),
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text = element_text(),
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panel.background = element_rect(colour = NA),
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plot.background = element_rect(colour = NA),
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panel.border = element_rect(colour = NA),
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axis.title = element_text(face = "bold",size = rel(1)),
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axis.title.y = element_text(angle=90,vjust =2),
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axis.title.x = element_text(vjust = -0.2),
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axis.text = element_text(),
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axis.line = element_line(colour="black"),
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axis.ticks = element_line(),
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panel.grid.major = element_line(colour="#f0f0f0"),
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panel.grid.minor = element_blank(),
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legend.key = element_rect(colour = NA),
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legend.position = "bottom",
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legend.direction = "horizontal",
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legend.key.size= unit(0.2, "cm"),
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legend.spacing = unit(0, "cm"),
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legend.title = element_text(face="italic"),
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plot.margin=unit(c(10,5,5,5),"mm"),
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strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
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strip.text = element_text(face="bold")
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))
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}
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scale_fill_Publication <- function(...){
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library(scales)
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discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
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}
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scale_colour_Publication <- function(...){
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discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
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}
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theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
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(theme_foundation(base_size=base_size, base_family=base_family)
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+ theme(plot.title = element_text(face = "bold",
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size = rel(1.2), hjust = 0.5),
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text = element_text(),
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panel.background = element_rect(colour = NA),
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plot.background = element_rect(colour = NA),
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panel.border = element_rect(colour = NA),
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axis.title = element_text(face = "bold",size = rel(1)),
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axis.title.y = element_text(angle=90,vjust =2),
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axis.title.x = element_text(vjust = -0.2),
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axis.text = element_text(),
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axis.line = element_line(colour="black"),
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axis.ticks = element_line(),
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panel.grid.major = element_line(colour="#f0f0f0"),
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panel.grid.minor = element_blank(),
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legend.key = element_rect(colour = NA),
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legend.position = "right",
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legend.direction = "vertical",
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legend.key.size= unit(0.5, "cm"),
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legend.spacing = unit(0, "cm"),
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legend.title = element_text(face="italic"),
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plot.margin=unit(c(10,5,5,5),"mm"),
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strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
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strip.text = element_text(face="bold")
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))
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}
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scale_fill_Publication <- function(...){
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discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
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}
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scale_colour_Publication <- function(...){
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discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
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}
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X1 <- read.csv(file = input_file1,stringsAsFactors=FALSE,header = TRUE)
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X2 <- read.csv(file = input_file2,stringsAsFactors=FALSE,header = TRUE)
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print(117)
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X <- merge(X1,X2,by ="Term_Avg",all=TRUE,suffixes = c("_X1","_X2"))
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gg <- ggplot(data = X,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_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_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep = "")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOntology.pdf",sep=""),width = 12,height = 8)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("130")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOntology.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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#ID aggravators and alleviators, regardless of whether they meet 2SD threshold
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X1_Specific_Aggravators <- X[which(X$Z_lm_L_Avg_X1 >= 2 & X$Z_lm_L_Avg_X2 < 2),]
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X1_Specific_Alleviators <- X[which(X$Z_lm_L_Avg_X1 <= -2 & X$Z_lm_L_Avg_X2 > -2),]
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X2_Specific_Aggravators <- X[which(X$Z_lm_L_Avg_X2 >= 2 & X$Z_lm_L_Avg_X1 < 2),]
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X2_Specific_Alleviators <- X[which(X$Z_lm_L_Avg_X2 <= -2 & X$Z_lm_L_Avg_X1 > -2),]
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Overlap_Aggravators <- X[which(X$Z_lm_L_Avg_X1 >= 2 & X$Z_lm_L_Avg_X2 >= 2),]
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Overlap_Alleviators <- X[which(X$Z_lm_L_Avg_X1 <= -2 & X$Z_lm_L_Avg_X2 <= -2),]
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X2_Specific_Aggravators_X1_Alleviatiors <- X[which(X$Z_lm_L_Avg_X2 >= 2 & X$Z_lm_L_Avg_X1 <= -2),]
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X2_Specific_Alleviators_X1_Aggravators <- X[which(X$Z_lm_L_Avg_X2 <= -2 & X$Z_lm_L_Avg_X1 >= 2),]
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print("155")
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X$Overlap_Avg <- NA
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try(X[X$Term_Avg %in% X1_Specific_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name1,"Specific_Deletion_Enhancers",sep="_"))
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try(X[X$Term_Avg %in% X1_Specific_Alleviators$Term_Avg,]$Overlap_Avg <- paste(Name1,"Specific_Deletion_Suppresors",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Specific_Deletion_Enhancers",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Alleviators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Specific_Deletion_Suppressors",sep="_"))
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try(X[X$Term_Avg %in% Overlap_Aggravators$Term_Avg,]$Overlap_Avg <- "Overlapping_Deletion_Enhancers")
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try(X[X$Term_Avg %in% Overlap_Alleviators$Term_Avg,]$Overlap_Avg <- "Overlapping_Deletion_Suppressors")
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try(X[X$Term_Avg %in% X2_Specific_Aggravators_X1_Alleviatiors$Term_Avg,]$Overlap_Avg <- paste(Name2,"Deletion_Enhancers",Name1,"Deletion_Suppressors",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Alleviators_X1_Aggravators$Term_Avg,]$Overlap_Avg <- paste(Name2,"Deletion_Suppressors",Name1,"Deletion_Enhancers",sep="_"))
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gg <- ggplot(data = X,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) + geom_point(shape=3) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap.pdf",sep=""),width = 12,height = 8)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#2-174")
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#2
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fname <- paste("/Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOverlap.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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x_rem2_gene <- X[X$NumGenes_Avg_X1 >= 2 & X$NumGenes_Avg_X2 >= 2,]
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#3
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gg <- ggplot(data = x_rem2_gene,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_above2genes.pdf",sep=""),width = 12,height = 8)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#3")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_byOverlap_above2genes.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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#4
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X_overlap_nothresold <- X[!(is.na(X$Overlap_Avg)),]
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gg <- ggplot(data = X_overlap_nothresold,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap_Avg,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep = "")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_Above2SD_ByOverlap.pdf",sep=""),width = 12,height = 8)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#4")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_Above2SD_ByOverlap.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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#only output GTA terms where average score is still above 2 after subtracting the SD
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#Z1 will ID aggravators, Z2 alleviators
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Z1 <- X
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Z1$L_Subtract_SD_X1 <- Z1$Z_lm_L_Avg_X1 - Z1$Z_lm_L_SD_X1
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Z1$L_Subtract_SD_X2 <- Z1$Z_lm_L_Avg_X2 - Z1$Z_lm_L_SD_X2
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Z2 <- X
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Z2$L_Subtract_SD_X1 <- Z1$Z_lm_L_Avg_X1 + Z1$Z_lm_L_SD_X1
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Z2$L_Subtract_SD_X2 <- Z1$Z_lm_L_Avg_X2 + Z1$Z_lm_L_SD_X2
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X1_Specific_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X1 >= 2 & Z1$L_Subtract_SD_X2 < 2),]
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X1_Specific_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X1 <= -2 & Z2$L_Subtract_SD_X2 > -2),]
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X2_Specific_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X2 >= 2 & Z1$L_Subtract_SD_X1 < 2),]
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X2_Specific_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z2$L_Subtract_SD_X1 > -2),]
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Overlap_Aggravators2 <- Z1[which(Z1$L_Subtract_SD_X1 >= 2 & Z1$L_Subtract_SD_X2 >= 2),]
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Overlap_Alleviators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z2$L_Subtract_SD_X1 <= -2),]
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X2_Specific_Aggravators2_X1_Alleviatiors2 <- Z1[which(Z1$L_Subtract_SD_X2 >= 2 & Z2$L_Subtract_SD_X1 <= -2),]
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X2_Specific_Alleviators2_X1_Aggravators2 <- Z2[which(Z2$L_Subtract_SD_X2 <= -2 & Z1$L_Subtract_SD_X1 >= 2),]
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X$Overlap <- NA
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try(X[X$Term_Avg %in% X1_Specific_Aggravators2$Term_Avg,]$Overlap <- paste(Name1,"Specific_Deletion_Enhancers",sep="_"))
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try(X[X$Term_Avg %in% X1_Specific_Alleviators2$Term_Avg,]$Overlap <- paste(Name1,"Specific_Deletion_Suppresors",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Aggravators2$Term_Avg,]$Overlap <- paste(Name2,"Specific_Deletion_Enhancers",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Alleviators2$Term_Avg,]$Overlap <- paste(Name2,"Specific_Deletion_Suppressors",sep="_"))
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try(X[X$Term_Avg %in% Overlap_Aggravators2$Term_Avg,]$Overlap <- "Overlapping_Deletion_Enhancers")
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try(X[X$Term_Avg %in% Overlap_Alleviators2$Term_Avg,]$Overlap <- "Overlapping_Deletion_Suppressors")
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try(X[X$Term_Avg %in% X2_Specific_Aggravators2_X1_Alleviatiors2$Term_Avg,]$Overlap <- paste(Name2,"Deletion_Enhancers",Name1,"Deletion_Suppressors",sep="_"))
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try(X[X$Term_Avg %in% X2_Specific_Alleviators2_X1_Aggravators2$Term_Avg,]$Overlap <- paste(Name2,"Deletion_Suppressors",Name1,"Deletion_Enhancers",sep="_"))
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#5
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X_abovethreshold <- X[!(is.na(X$Overlap)),]
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gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold.pdf",sep=""),width = 12,height = 8)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#5")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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#6
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gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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geom_text(aes(label=Term_Avg),nudge_y = 0.25,size=2) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_names.pdf",sep=""),width = 20,height = 20)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#6")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_names.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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X_abovethreshold$X1_Rank <- NA
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X_abovethreshold$X1_Rank <- rank(-X_abovethreshold$Z_lm_L_Avg_X1,ties.method = "random")
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X_abovethreshold$X2_Rank <- NA
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X_abovethreshold$X2_Rank <- rank(-X_abovethreshold$Z_lm_L_Avg_X2,ties.method = "random")
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#7
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gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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geom_text(aes(label=X1_Rank),nudge_y = 0.25,size=4) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX1.pdf",sep=""),width = 15,height = 15)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#7")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX1.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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#8
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gg <- ggplot(data = X_abovethreshold,aes(x=Z_lm_L_Avg_X1,y=Z_lm_L_Avg_X2,color=Overlap,Term=Term_Avg,Genes=Genes_Avg_X1,NumGenes=NumGenes_Avg_X1,AllPossibleGenes=AllPossibleGenes_Avg_X1,SD_1=Z_lm_L_SD_X1,SD_2=Z_lm_L_SD_X2)) +
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xlab(paste("GO Term Avg lm Z for ",Name1,sep="")) +
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geom_text(aes(label=X2_Rank),nudge_y = 0.25,size=4) +
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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) +
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ylab(paste("GO Term Avg lm Z for ",Name2,sep="")) + ggtitle(paste("Comparing Average GO Term Z lm for ",Name1," vs. ",Name2,sep="")) +
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theme_Publication_legend_right()
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pdf(paste(outputpath,"/","Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX2.pdf",sep=""),width = 15,height = 15)
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gg
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dev.off()
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pgg <- ggplotly(gg)
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#pgg
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print("#8")
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fname <- paste("Scatter_lm_GTA_Averages_",Name1,"_vs_",Name2,"_All_ByOverlap_AboveThreshold_numberedX2.html",sep="")
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print(fname)
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htmlwidgets::saveWidget(pgg, file.path(getwd(),fname))
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file.rename(from = file.path(getwd(),fname), to = file.path(pairDirL,fname))
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print("write csv files L")
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write.csv(x=X,file = paste(outputpath,"/All_GTA_Avg_Scores_",Name1,"_vs_",Name2,".csv",sep=""),row.names = FALSE)
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write.csv(x=X_abovethreshold,file = paste(getwd(),"/",outputpath,"/","AboveThreshold_GTA_Avg_Scores_",Name1,"_vs_",Name2,".csv",sep=""),row.names = FALSE)
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#End of L GTA Pairwise Compare
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