#!/usr/bin/env Rscript
# NOTE: The script now has 6 optional arguments:
#   1. Path to input easy results file
#   2. /output/ directory
#   3. Path to StudyInfo.csv
#   4. Path to SGDgeneList
#   5. Standard deviation value
#   6. The experiment number (Exp# directory)

library(ggplot2)
library(plyr)
library(extrafont)
library(gridExtra)
library(gplots)
library(RColorBrewer)
library(stringr)
library(gdata)
library(plotly)
library(htmlwidgets)

# Parse arguments
args <- commandArgs(TRUE)
inputFile <- file.path(args[1])

# Set output dir
if (length(args) >= 2) {
  outDir <- file.path(args[2])
} else {
  outDir <- "/ZScores/" # for legacy workflow
}

# Set StudyInfo file path
if (length(args) >= 3) {
  studyInfo <- file.path(args[3])
} else {
  studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow
}

# Set SGDgeneList file path
if (length(args) >= 4) {
  SGDgeneList <- file.path(args[4])
} else {
  SGDgeneList <- "../Code/SGD_features.tab" # for legacy workflow
}

# Set standard deviation
if (length(args) >= 5) {
  delBGFactor <- args[5]
} else {
  # User prompt for std multiplier Value
  print("Enter a Standard Deviation value for noise filter")
  print("Sean Santos recommends 3 or 5")
  delBGFactor <- readLines(file("stdin"), n = 1L)
}
delBGFactor <- as.numeric(delBGFactor)
if (is.na(delBGFactor)) {
  delBGFactor <- 3 # recommended by Sean
}
print(paste("The Standard Deviation Value is:", delBGFactor))

# Set experiment #
if (length(args) >= 6) {
  expNumber <- args[6]
} else {
  # User prompt for std multiplier Value
  print("Enter the experiment number (Exp# directory)")
  expNumber <- readLines(file("stdin"), n = 1L)
}
expNumber <- as.numeric(expNumber)

outDir_QC <- file.path(outDir, "QC")

if (!file.exists(outDir)) {
  dir.create(outDir)
}

if (!file.exists(outDir_QC)) {
  dir.create(file.path(outDir_QC))
}

options(width = 1000)
ls.str()

# Write delBJFactor to the StudyInfo file
# TODO we probably shouldn't be doing this, need one source of truth
# TODO disabling this for now
# Labels <- read.csv(file = studyInfo, stringsAsFactors = FALSE)  # sep = ","
# Labels[expNumber, 3] <- delBGFactor
# write.csv(Labels, file = studyInfo, row.names = FALSE)

# Begin User Data Selection Section

# Read in the data
X <- read.delim(inputFile, skip = 2, as.is = TRUE, row.names = 1, strip.white = TRUE)
X <- X[!(X[[1]] %in% c("", "Scan")), ]
# X <- X[!(X[[1]]%in%c(61:76)), ]  #Remove dAmp plates which are Scans 61 thru 76
# X <- X[which(X$Specifics == "WT"), ]
# X_length <- length(X[1, ])
# X_end <- length(X[1, ]) - 2
# X <- X[, c(1:42, X_end:X_length)]

# Use numeric data to perform operations
X$Col <- as.numeric(X$Col)
X$Row <- as.numeric(X$Row)
X$l <- as.numeric(X$l)
X$K <- as.numeric(X$K)
X$r <- as.numeric(X$r)
X$Scan <- as.numeric(X$Scan)
X$AUC <- as.numeric(X$AUC)
X$LstBackgrd <- as.numeric(X$LstBackgrd)
X$X1stBackgrd <- as.numeric(X$X1stBackgrd)

# Sometimes the non-varying drug is in the 'Drug' col vs the 'Modifier1' col
# as was the case in Gemcitabin and Cytarabin experiments.
# The following allows user to rename columns so as to get the appropriate
# data where it needs to be for the script to run properly.

# colnames(X)[7]  <- "Modifier1"
# colnames(X)[8]  <- "Conc1"
# colnames(X)[10]  <- "Drug"
# colnames(X)[11]  <- "Conc"

# Set the OrfRep to YDL227C for the ref data
X[X$ORF == "YDL227C", ]$OrfRep <- "YDL227C"

# Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used.
# That is the first DM plates are removed from the data set with the following.
# This removes data with dox == 0 leaving gene expression on with four different concentrations of Gemcytabin
# X <- X[X$Conc1 != "0ug/ml", ]
X <- X[X$Drug != "BMH21", ] # this removes data concerning BMH21 for this experiment

# Mert placed the "bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates.
# This code removes those spots from the data set used for the interaction analysis.
# Dr.Hartman feels that these donot effect Zscores significantly and so "non-curated" files were used.
# try(X <- X[X$ORF != "bad_spot", ])

# Get total number of drug concentrations
total_conc_nums <- length(unique(X$Conc))

# Function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
numextract <- function(string) {
  str_extract(string, "\\-*\\d+\\.*\\d*")
}

# Generate a new column with the numeric drug concs
X$Conc_Num <- as.numeric(numextract(X$Conc))

# Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1

# Get the max factor for concentration
MAX_CONC <- max(X$Conc_Num_Factor)

# If treating numbers not as factors uncomment next line and comment out previous line
# MAX_CONC <- max(X$Conc_Num)

# Remove wells with problems for making graphs and to not include in summary statistics
X <- X[X$Gene != "BLANK", ]
X <- X[X$Gene != "Blank", ]
X <- X[X$ORF != "Blank", ]
X <- X[X$Gene != "blank", ]
# X <- X[X$Gene != "HO", ]
Xbu <- X

# Use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script).
# This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
# Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to overwrite the RF1&RF2 geneNames
# in the Z_lm_L, K, r&AUC output values. Values correlated well but were off by a multiplier factor.
genes <- data.frame(read.delim(
  file = SGDgeneList, quote = "", header = FALSE, colClasses = c(rep("NULL", 3), rep("character", 2), rep("NULL", 11))))
for (i in 1:length(X[, 14])) {
  ii <- as.numeric(i)
  line_num <- match(X[ii, 14], genes[, 1], nomatch = 1)
  OrfRepColNum <- as.numeric(match("OrfRep", names(X)))
  if (X[ii, OrfRepColNum] != "YDL227C") {
    X[ii, 15] <- genes[line_num, 2]
  }
  if ((X[ii, 15] == "") || (X[ii, 15] == "OCT1")) {
    X[ii, 15] <- X[ii, OrfRepColNum]
  }
}

Xblankreplace <- X
# X = Xbu # for restore testing restore X if geneName 'Match' routine needs changing

# Remove dAmPs
# jlh confirmed to leave dAmps in so comment out this section
# DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
# Damps <- read.delim(DAmPs_List, header = F)

# X <- X[!(X$ORF %in% Damps$V1), ]  # fix this to Damps[, 1]
# XafterDampsRM = X  # backup for debugging

# Begin Graphics Boiler Plate Section
# theme elements for plots
theme_publication <- function(base_size = 14, base_family = "sans") {
  library(grid)
  library(ggthemes)
  (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")), ...)
}

# Print timestamp for initial time the code starts
timestamp()

# Begin QC Section
# Part 2 - Quality control
# Print quality control graphs for each dataset before removing contaminated data
# and before adjusting missing data to max theoretical values

# Plate analysis plot
# Plate analysis is a quality check to identify plate effects containing anomalies

Plate_Analysis_L <-
  ggplot(X, aes(Scan, l, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean, 
    fun.min = function(x) mean(x) - sd(x), 
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) + 
  stat_summary(fun = mean, geom = "point", size = 0.6) +
  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_publication()

Plate_Analysis_K <-
  ggplot(X, aes(Scan, K, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(fun = mean, geom = "point", size = 0.6) +
  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_publication()

Plate_Analysis_r <-
  ggplot(X, aes(Scan, r, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(fun = mean, geom = "point", size = 0.6) +
  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_publication()

Plate_Analysis_AUC <-
  ggplot(X, aes(Scan, AUC, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(fun = mean, geom = "point", size = 0.6) +
  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_publication()

Plate_Analysis_L_Box <-
  ggplot(X, aes(as.factor(Scan), l, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for L before quality control") +
  theme_publication()

Plate_Analysis_K_Box <-
  ggplot(X, aes(as.factor(Scan), K, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for K before quality control") +
  theme_publication()

Plate_Analysis_r_Box <-
  ggplot(X, aes(as.factor(Scan), r, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for r before quality control") +
  theme_publication()

Plate_Analysis_AUC_Box <-
  ggplot(X, aes(as.factor(Scan), AUC, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for AUC before quality control") +
  theme_publication()

# Quality control - values with a high delta background likely have heavy contamination
# Check the frequency of these values
# Report the L and K values of these spots
# Report the number to be removed based on the Delta_Background_Tolerance
X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd

# Raw l vs K before QC
Raw_l_vs_K_beforeQC <-
  ggplot(X, aes(l, K, color = as.factor(Conc_Num))) +
  geom_point(aes(ORF = ORF, Gene = Gene, Delta_Backgrd = Delta_Backgrd), shape = 3) +
  ggtitle("Raw L vs K before QC") +
  theme_publication_legend_right()

pdf(file.path(outDir_QC, "Raw_L_vs_K_beforeQC.pdf"), width = 12, height = 8)
Raw_l_vs_K_beforeQC

dev.off()

pgg <- ggplotly(Raw_l_vs_K_beforeQC)
plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_beforeQC.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)

# Set delta background tolerance based on 3 sds from the mean delta background
Delta_Background_Tolerance <- mean(X$Delta_Backgrd) + (delBGFactor * sd(X$Delta_Backgrd))
# Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
sprintf("Delta_Background_Tolerance is %f", Delta_Background_Tolerance)

Plate_Analysis_Delta_Backgrd <-
  ggplot(X, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2, position = "jitter") +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(fun = mean, geom = "point", size = 0.6) +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") +
  theme_publication()

Plate_Analysis_Delta_Backgrd_Box <-
  ggplot(X, aes(as.factor(Scan), Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") +
  theme_publication()

X_Delta_Backgrd_above_Tolerance <-
  X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]
X_Delta_Backgrd_above_Tolerance_K_halfmedian <-
  (median(X_Delta_Backgrd_above_Tolerance$K, na.rm = TRUE)) / 2
X_Delta_Backgrd_above_Tolerance_L_halfmedian <-
  (median(X_Delta_Backgrd_above_Tolerance$l, na.rm = TRUE)) / 2
X_Delta_Backgrd_above_Tolerance_toRemove <-
  dim(X_Delta_Backgrd_above_Tolerance)[1]

X_Delta_Backgrd_above_Tolerance_L_vs_K <-
  ggplot(X_Delta_Backgrd_above_Tolerance, aes(l, K, color = as.factor(Conc_Num))) +
  geom_point(aes(ORF = ORF, Gene = Gene, Delta_Backgrd = Delta_Backgrd), shape = 3) +
  ggtitle(paste("Raw L vs K for strains above delta background threshold of", Delta_Background_Tolerance, "or above")) +
  annotate("text", x = X_Delta_Backgrd_above_Tolerance_L_halfmedian, y = X_Delta_Backgrd_above_Tolerance_K_halfmedian,
    label = paste("Strains above delta background tolerance = ", X_Delta_Backgrd_above_Tolerance_toRemove)
  ) +
  theme_publication_legend_right()

pdf(file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf"), width = 12, height = 8)
X_Delta_Backgrd_above_Tolerance_L_vs_K

dev.off()

pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html")
saveWidget(pgg, file = plotly_path, selfcontained = TRUE)

# Frequency plot for all data vs. the delta_background
DeltaBackground_Frequency_Plot <- ggplot(X, aes(Delta_Backgrd, color = as.factor(Conc_Num))) + geom_density() +
  ggtitle("Density plot for Delta Background by Conc All Data") + theme_publication_legend_right()

# Bar plot for all data vs. the delta_background
DeltaBackground_Bar_Plot <- ggplot(X, aes(Delta_Backgrd, color = as.factor(Conc_Num))) + geom_bar() +
  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_publication_legend_right()

pdf(file.path(outDir_QC, "Frequency_Delta_Background.pdf"), width = 12, height = 8)
print(DeltaBackground_Frequency_Plot)
print(DeltaBackground_Bar_Plot)
dev.off()

# Need to identify missing data, and differentiate between this data and removed data
# so the removed data can get set to NA and the missing data can get set to max theoretical values
# 1 for missing data, 0 for non missing data
# Use "NG" for NoGrowth rather than "missing"
X$NG <- 0
try(X[X$l == 0 & !is.na(X$l), ]$NG <- 1)

# 1 for removed data, 0 non removed data
# Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
X$DB <- 0
try(X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]$DB <- 1)

# Replace the CPPs for l, r, AUC and K (must be last!) for removed data
try(X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]$l <- NA)
try(X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]$r <- NA)
try(X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]$AUC <- NA)
try(X[X$Delta_Backgrd >= Delta_Background_Tolerance, ]$K <- NA)

# QC Plots
Plate_Analysis_L_afterQC <- ggplot(X, aes(Scan, l, color = as.factor(Conc_Num))) + geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_publication()

Plate_Analysis_K_afterQC <-
  ggplot(X, aes(Scan, K, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_publication()

Plate_Analysis_r_afterQC <-
  ggplot(X, aes(Scan, r, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_publication()

Plate_Analysis_AUC_afterQC <-
  ggplot(X, aes(Scan, AUC, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_publication()

Plate_Analysis_Delta_Backgrd_afterQC <-
  ggplot(X, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") +
  theme_publication()

Plate_Analysis_L_Box_afterQC <-
  ggplot(X, aes(as.factor(Scan), l, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for L after quality control") +
  theme_publication()

Plate_Analysis_K_Box_afterQC <-
  ggplot(X, aes(as.factor(Scan), K, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for K after quality control") +
  theme_publication()

Plate_Analysis_r_Box_afterQC <-
  ggplot(X, aes(as.factor(Scan), r, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for r after quality control") +
  theme_publication()

Plate_Analysis_AUC_Box_afterQC <-
  ggplot(X, aes(as.factor(Scan), AUC, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for AUC after quality control") +
  theme_publication()

Plate_Analysis_Delta_Backgrd_Box_afterQC <-
  ggplot(X, aes(as.factor(Scan), Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") +
  theme_publication()

# Print the plate analysis data before and after QC
pdf(file.path(outDir_QC, "Plate_Analysis.pdf"), width = 14, height = 9)
Plate_Analysis_L
Plate_Analysis_L_afterQC
Plate_Analysis_K
Plate_Analysis_K_afterQC
Plate_Analysis_r
Plate_Analysis_r_afterQC
Plate_Analysis_AUC
Plate_Analysis_AUC_afterQC
Plate_Analysis_Delta_Backgrd
Plate_Analysis_Delta_Backgrd_afterQC
dev.off()

# Print the plate analysis data before and after QC
pdf(file.path(outDir_QC, "Plate_Analysis_Boxplots.pdf"), width = 18, height = 9)
Plate_Analysis_L_Box
Plate_Analysis_L_Box_afterQC
Plate_Analysis_K_Box
Plate_Analysis_K_Box_afterQC
Plate_Analysis_r_Box
Plate_Analysis_r_Box_afterQC
Plate_Analysis_AUC_Box
Plate_Analysis_AUC_Box_afterQC
Plate_Analysis_Delta_Backgrd_Box
Plate_Analysis_Delta_Backgrd_Box_afterQC
dev.off()

# Remove the zero values and print plate analysis
X_noZero <- X[which(X$l > 0), ]
Plate_Analysis_L_afterQC_Z <-
  ggplot(X_noZero, aes(Scan, l, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for L after quality control") +
  theme_publication()

Plate_Analysis_K_afterQC_Z <-
  ggplot(X_noZero, aes(Scan, K, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for K after quality control") +
  theme_publication()

Plate_Analysis_r_afterQC_Z <-
  ggplot(X_noZero, aes(Scan, r, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for r after quality control") +
  theme_publication()

Plate_Analysis_AUC_afterQC_Z <-
  ggplot(X_noZero, aes(Scan, AUC, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for AUC after quality control") +
  theme_publication()

Plate_Analysis_Delta_Backgrd_afterQC_Z <-
  ggplot(X_noZero, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_point(shape = 3, size = 0.2) +
  stat_summary(
    fun = mean,
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    geom = "errorbar"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 0.6
  ) +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") +
  theme_publication()

Plate_Analysis_L_Box_afterQC_Z <-
  ggplot(X_noZero, aes(as.factor(Scan), l, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for L after quality control") +
  theme_publication()

Plate_Analysis_K_Box_afterQC_Z <-
  ggplot(X_noZero, aes(as.factor(Scan), K, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for K after quality control") +
  theme_publication()

Plate_Analysis_r_Box_afterQC_Z <-
  ggplot(X_noZero, aes(as.factor(Scan), r, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for r after quality control") +
  theme_publication()

Plate_Analysis_AUC_Box_afterQC_Z <-
  ggplot(X_noZero, aes(as.factor(Scan), AUC, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for AUC after quality control") +
  theme_publication()

Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <-
  ggplot(X_noZero, aes(as.factor(Scan), Delta_Backgrd, color = as.factor(Conc_Num))) +
  geom_boxplot() +
  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") +
  theme_publication()

# Print the plate analysis data before and after QC
pdf(file.path(outDir_QC, "Plate_Analysis_noZeros.pdf"), width = 14, height = 9)
Plate_Analysis_L_afterQC_Z
Plate_Analysis_K_afterQC_Z
Plate_Analysis_r_afterQC_Z
Plate_Analysis_AUC_afterQC_Z
Plate_Analysis_Delta_Backgrd_afterQC_Z
dev.off()

# Print the plate analysis data before and after QC
pdf(file.path(outDir_QC, "Plate_Analysis_noZeros_Boxplots.pdf"), width = 18, height = 9)
Plate_Analysis_L_Box_afterQC_Z
Plate_Analysis_K_Box_afterQC_Z
Plate_Analysis_r_Box_afterQC_Z
Plate_Analysis_AUC_Box_afterQC_Z
Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
dev.off()

# Remove dataset with zeros removed
rm(X_noZero)

# X_test_missing_and_removed <- X[X$Removed == 1, ]

# Calculate summary statistics for all strains, including both background and the deletions
X_stats_ALL <- ddply(
  X,
  c("Conc_Num", "Conc_Num_Factor"),
  summarise,
  N = (length(l)),
  mean_L = mean(l, na.rm = TRUE),
  median_L = median(l, na.rm = TRUE),
  max_L = max(l, na.rm = TRUE),
  min_L = min(l, na.rm = TRUE),
  sd_L = sd(l, na.rm = TRUE),
  se_L = sd_L / sqrt(N - 1),
  mean_K = mean(K, na.rm = TRUE),
  median_K = median(K, na.rm = TRUE),
  max_K = max(K, na.rm = TRUE),
  min_K = min(K, na.rm = TRUE),
  sd_K = sd(K, na.rm = TRUE),
  se_K = sd_K / sqrt(N - 1),
  mean_r = mean(r, na.rm = TRUE),
  median_r = median(r, na.rm = TRUE),
  max_r = max(r, na.rm = TRUE),
  min_r = min(r, na.rm = TRUE),
  sd_r = sd(r, na.rm = TRUE),
  se_r = sd_r / sqrt(N - 1),
  mean_AUC = mean(AUC, na.rm = TRUE),
  median_AUC = median(AUC, na.rm = TRUE),
  max_AUC = max(AUC, na.rm = TRUE),
  min_AUC = min(AUC, na.rm = TRUE),
  sd_AUC = sd(AUC, na.rm = TRUE),
  se_AUC = sd_AUC / sqrt(N - 1)
)

# print(X_stats_ALL_L)
write.csv(X_stats_ALL, file.path(outDir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)

# Part 3 - Generate summary statistics and calculate the max theoretical L value
# Calculate the Z score at each drug conc for each deletion strain

# Get the background strains - can be modified to take another argument but for most screens will just be YDL227C
Background_Strains <- c("YDL227C")

# First part of loop will go through for each background strain
# In most cases there will only be one YDL227C
for (s in Background_Strains) {
  X_Background <- X[X$OrfRep == s, ]

  # If there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
  # we may want to consider in some cases giving the max high value to L depending on the data type
  if (table(X_Background$l)[1] == 0) {
    X_Background[X_Background$l == 0, ]$l <- NA
    X_Background[X_Background$K == 0, ]$K <- NA
    X_Background[X_Background$r == 0, ]$r <- NA
    X_Background[X_Background$AUC == 0, ]$AUC <- NA
  }

  X_Background <- X_Background[!is.na(X_Background$l), ]

  # Get summary stats for L, K, R, AUC
  X_stats_BY_L <- ddply(
    X_Background,
    c("OrfRep", "Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = (length(l)),
    mean = mean(l, na.rm = TRUE),
    median = median(l, na.rm = TRUE),
    max = max(l, na.rm = TRUE),
    min = min(l, na.rm = TRUE),
    sd = sd(l, na.rm = TRUE),
    se = sd / sqrt(N - 1)
  )

  print(X_stats_BY_L)
  X1_SD <- max(X_stats_BY_L$sd)

  X_stats_BY_K <- ddply(
    X_Background,
    c("OrfRep", "Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = (length(K)),
    mean = mean(K, na.rm = TRUE),
    median = median(K, na.rm = TRUE),
    max = max(K, na.rm = TRUE),
    min = min(K, na.rm = TRUE),
    sd = sd(K, na.rm = TRUE),
    se = sd / sqrt(N - 1)
  )
  
  X1_SD_K <- max(X_stats_BY_K$sd)
  
  X_stats_BY_r <- ddply(
    X_Background,
    c("OrfRep", "Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = length(r),
    mean = mean(r, na.rm = TRUE),
    median = median(r, na.rm = TRUE),
    max = max(r, na.rm = TRUE),
    min = min(r, na.rm = TRUE),
    sd = sd(r, na.rm = TRUE),
    se = sd / sqrt(N - 1)
  )
  
  X1_SD_r <- max(X_stats_BY_r$sd)
  
  X_stats_BY_AUC <- ddply(
    X_Background,
    c("OrfRep", "Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = length(AUC),
    mean = mean(AUC, na.rm = TRUE),
    median = median(AUC, na.rm = TRUE),
    max = max(AUC, na.rm = TRUE),
    min = min(AUC, na.rm = TRUE),
    sd = sd(AUC, na.rm = TRUE),
    se = sd / sqrt(N - 1)
  )
  
  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
  
  X_stats_BY <- ddply(
    X_Background,
    c("OrfRep", "Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = (length(l)),
    mean_L = mean(l, na.rm = TRUE),
    median_L = median(l, na.rm = TRUE),
    max_L = max(l, na.rm = TRUE),
    min_L = min(l, na.rm = TRUE),
    sd_L = sd(l, na.rm = TRUE),
    se_L = sd_L / sqrt(N - 1),
    mean_K = mean(K, na.rm = TRUE),
    median_K = median(K, na.rm = TRUE),
    max_K = max(K, na.rm = TRUE),
    min_K = min(K, na.rm = TRUE),
    sd_K = sd(K, na.rm = TRUE),
    se_K = sd_K / sqrt(N - 1),
    mean_r = mean(r, na.rm = TRUE),
    median_r = median(r, na.rm = TRUE),
    max_r = max(r, na.rm = TRUE),
    min_r = min(r, na.rm = TRUE),
    sd_r = sd(r, na.rm = TRUE),
    se_r = sd_r / sqrt(N - 1),
    mean_AUC = mean(AUC, na.rm = TRUE),
    median_AUC = median(AUC, na.rm = TRUE),
    max_AUC = max(AUC, na.rm = TRUE),
    min_L = min(AUC, na.rm = TRUE),
    sd_AUC = sd(AUC, na.rm = TRUE),
    se_AUC = sd_AUC / sqrt(N - 1)
  )
  
  write.csv(X_stats_BY, file.path(outDir, "SummaryStats_BackgroundStrains.csv"), row.names = FALSE)
  
  # Calculate the max theoretical L values
  # Only look for max values when K is within 2SD of the ref strain
  for (q in unique(X$Conc_Num_Factor)) {
    if (q == 0) {
      X_within_2SD_K <-
        X[X$Conc_Num_Factor == q, ]
      X_within_2SD_K <-
        X_within_2SD_K[!is.na(X_within_2SD_K$l), ]
      X_stats_TEMP_K <-
        X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q, ]
      X_within_2SD_K <-
        X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2 * X_stats_TEMP_K$sd[1])), ]
      X_within_2SD_K <-
        X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2 * X_stats_TEMP_K$sd[1])), ]
      X_outside_2SD_K <-
        X[X$Conc_Num_Factor == q, ]
      X_outside_2SD_K <-
        X_outside_2SD_K[!is.na(X_outside_2SD_K$l), ]
      # X_outside_2SD_K_Temp <-
      #   X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q, ]
      X_outside_2SD_K <-
        X_outside_2SD_K[
          X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2 * X_stats_TEMP_K$sd[1])) |
          X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2 * X_stats_TEMP_K$sd[1])), ]
      # X_outside_2SD_K <-
      #   X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])), ]
    }
    if (q > 0) {
      X_within_2SD_K_temp <-
        X[X$Conc_Num_Factor == q, ]
      X_within_2SD_K_temp <-
        X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l), ]
      X_stats_TEMP_K <-
        X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q, ]
      X_within_2SD_K_temp <-
        X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2 * X_stats_TEMP_K$sd[1])), ]
      X_within_2SD_K_temp <-
        X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2 * X_stats_TEMP_K$sd[1])), ]
      X_within_2SD_K <-
        rbind(X_within_2SD_K, X_within_2SD_K_temp)
      X_outside_2SD_K_temp <-
        X[X$Conc_Num_Factor == q, ]
      X_outside_2SD_K_temp <-
        X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l), ]
      # X_outside_2SD_K_Temp <-
      #   X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q, ]
      X_outside_2SD_K_temp <-
        X_outside_2SD_K_temp[
          X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2 * X_stats_TEMP_K$sd[1])) |
          X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2 * X_stats_TEMP_K$sd[1])), ]
      # X_outside_2SD_K_temp <-
      #   X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) , ]
      X_outside_2SD_K <-
        rbind(X_outside_2SD_K, X_outside_2SD_K_temp)
    }
  }

  X_stats_BY_L_within_2SD_K <- ddply(
    X_within_2SD_K,
    c("Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = (length(l)),
    mean = mean(l),
    median = median(l),
    max = max(l, na.rm = TRUE),
    min = min(l, na.rm = TRUE),
    sd = sd(l),
    se = sd / sqrt(N - 1),
    z_max = (max - mean) / sd
  )

  print(X_stats_BY_L_within_2SD_K)

  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)

  write.csv(
    X_stats_BY_L_within_2SD_K,
    file.path(outDir_QC, "Max_Observed_L_Vals_for_spots_within_2SD_K.csv"),
    row.names = FALSE
  )
  
  X_stats_BY_L_outside_2SD_K <- ddply(
    X_outside_2SD_K,
    c("Conc_Num", "Conc_Num_Factor"),
    summarise,
    N = (length(l)),
    mean = mean(l),
    median = median(l),
    max = max(l, na.rm = TRUE),
    min = min(l, na.rm = TRUE),
    sd = sd(l),
    se = sd / sqrt(N - 1)
  )

  print(X_stats_BY_L_outside_2SD_K)
  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)

  # X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l, ]
  Outside_2SD_K_L_vs_K <- 
    ggplot(X_outside_2SD_K, aes(l, K, color = as.factor(Conc_Num))) +
    geom_point(aes(ORF = ORF, Gene = Gene, Delta_Backgrd = Delta_Backgrd), shape = 3) +
    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") +
    theme_publication_legend_right()
  
  pdf(file.path(outDir_QC, "Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf"), width = 10, height = 8)
  print(Outside_2SD_K_L_vs_K)

  dev.off()

  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
  plotly_path <- file.path(outDir_QC, "RawL_vs_K_for_strains_outside_2SD_K.html")
  saveWidget(pgg, file = plotly_path, selfcontained = TRUE)

  Outside_2SD_K_delta_background_vs_K <-
    ggplot(X_outside_2SD_K, aes(Delta_Backgrd, K, color = as.factor(Conc_Num))) +
    geom_point(aes(l = l, ORF = ORF, Gene = Gene), shape = 3, position = "jitter") +
    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") +
    theme_publication_legend_right()

  pdf(file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf"), width = 10, height = 8)
  Outside_2SD_K_delta_background_vs_K
  dev.off()
  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)

  # pgg
  plotly_path <- file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_outside_2SD_K.html")
  saveWidget(pgg, file = plotly_path, selfcontained = TRUE)

  # Get the background strain mean values at the no drug conc to calculate shift
  Background_L <- X_stats_BY_L$mean[1]
  Background_K <- X_stats_BY_K$mean[1]
  Background_r <- X_stats_BY_r$mean[1]
  Background_AUC <- X_stats_BY_AUC$mean[1]

  # Create empty plots for plotting element
  p_l <- ggplot()
  p_K <- ggplot()
  p_r <- ggplot()
  p_AUC <- ggplot()

  p_rf_l <- ggplot()
  p_rf_K <- ggplot()
  p_rf_r <- ggplot()
  p_rf_AUC <- ggplot()

  # Get only the deletion strains
  X2 <- X
  X2 <- X2[X2$OrfRep != "YDL227C", ]

  # If set to max theoretical value, add a 1 to SM, if not, leave as 0
  # SM = Set to Max
  X2$SM <- 0
  # Set the missing values to the highest theoretical value at each drug conc for L
  # Leave other values as 0 for the max/min
  for (i in 1:length(unique(X2$Conc_Num))) {
    Concentration <- unique(X2$Conc_Num)[i]
    X2_temp <- X2[X2$Conc_Num == Concentration, ]
    if (Concentration == 0) {
      X2_new <- X2_temp
      sprintf("Check loop order, conc = %f", Concentration)
    }
    if (Concentration > 0) {
      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l), ]$SM <- 1)
      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
      # X2_temp[X2_temp$K == 0, ]$K <- X_stats_ALL_K$max[i]
      # X2_temp[X2_temp$r == 0, ]$r <- X_stats_ALL_r$max[i]
      # X2_temp[X2_temp$AUC == 0, ]$AUC <- X_stats_ALL_AUC$max[i]
      sprintf("Check loop order, conc = %f", Concentration)
      X2_new <- rbind(X2_new, X2_temp)
    }
  }

  X2 <- X2_new

  # Get only the RF strains
  X2_RF <- X
  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C", ]
  # If set to max theoretical value, add a 1 to SM, if not, leave as 0
  # SM = Set to Max
  X2_RF$SM <- 0
  # Set the missing values to the highest theoretical value at each drug conc for L
  # Leave other values as 0 for the max/min
  for (i in 1:length(unique(X2_RF$Conc_Num))) {
    Concentration <- unique(X2_RF$Conc_Num)[i]
    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration, ]
    if (Concentration == 0) {
      X2_RF_new <- X2_RF_temp
      sprintf("Check loop order, conc = %f", Concentration)
    }
    if (Concentration > 0) {
      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l), ]$SM <- 1)
      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
      sprintf("If error, refs have no L values outside theoretical max L, for REFs, conc = %f", Concentration)
      X2_RF_new <- rbind(X2_RF_new, X2_RF_temp)
    }
  }
  X2_RF <- X2_RF_new

  # Part 4 Get the RF Z score values
  # Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
  X2_RF$OrfRep <- paste(X2_RF$OrfRep, X2_RF$Gene, X2_RF$Num., sep = "_")

  num_genes_RF <- length(unique(X2_RF$OrfRep))
  # print(num_genes_RF)

  # Create the output data.frame containing columns for each RF strain
  InteractionScores_RF <- unique(X2_RF["OrfRep"])
  # InteractionScores_RF$Gene <- unique(X2$Gene)
  InteractionScores_RF$Gene <- NA
  InteractionScores_RF$Raw_Shift_L <- NA
  InteractionScores_RF$Z_Shift_L <- NA
  InteractionScores_RF$lm_Score_L <- NA
  InteractionScores_RF$Z_lm_L <- NA
  InteractionScores_RF$R_Squared_L <- NA
  InteractionScores_RF$Sum_Z_Score_L <- NA
  InteractionScores_RF$Avg_Zscore_L <- NA
  InteractionScores_RF$Raw_Shift_K <- NA
  InteractionScores_RF$Z_Shift_K <- NA
  InteractionScores_RF$lm_Score_K <- NA
  InteractionScores_RF$Z_lm_K <- NA
  InteractionScores_RF$R_Squared_K <- NA
  InteractionScores_RF$Sum_Z_Score_K <- NA
  InteractionScores_RF$Avg_Zscore_K <- NA
  InteractionScores_RF$Raw_Shift_r <- NA
  InteractionScores_RF$Z_Shift_r <- NA
  InteractionScores_RF$lm_Score_r <- NA
  InteractionScores_RF$Z_lm_r <- NA
  InteractionScores_RF$R_Squared_r <- NA
  InteractionScores_RF$Sum_Z_Score_r <- NA
  InteractionScores_RF$Avg_Zscore_r <- NA
  InteractionScores_RF$Raw_Shift_AUC <- NA
  InteractionScores_RF$Z_Shift_AUC <- NA
  InteractionScores_RF$lm_Score_AUC <- NA
  InteractionScores_RF$Z_lm_AUC <- NA
  InteractionScores_RF$R_Squared_AUC <- NA
  InteractionScores_RF$Sum_Z_Score_AUC <- NA
  InteractionScores_RF$Avg_Zscore_AUC <- NA
  InteractionScores_RF$NG <- NA
  InteractionScores_RF$SM <- NA

  for (i in 1:num_genes_RF) {
    # Get each deletion strain ORF
    Gene_Sel <- unique(X2_RF$OrfRep)[i]
    # Extract only the current deletion strain and its data
    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel, ]

    X_stats_interaction <- ddply(
      X_Gene_Sel,
      c("OrfRep", "Gene", "Conc_Num", "Conc_Num_Factor"),
      summarise,
      N = (length(l)),
      mean_L = mean(l, na.rm = TRUE),
      median_L = median(l, na.rm = TRUE),
      sd_L = sd(l, na.rm = TRUE),
      se_L = sd_L / sqrt(N - 1),
      mean_K = mean(K, na.rm = TRUE),
      median_K = median(K, na.rm = TRUE),
      sd_K = sd(K, na.rm = TRUE),
      se_K = sd_K / sqrt(N - 1),
      mean_r = mean(r, na.rm = TRUE),
      median_r = median(r, na.rm = TRUE),
      sd_r = sd(r, na.rm = TRUE),
      se_r = sd_r / sqrt(N - 1),
      mean_AUC = mean(AUC, na.rm = TRUE),
      median_AUC = median(AUC, na.rm = TRUE),
      sd_AUC = sd(AUC, na.rm = TRUE),
      se_AUC = sd_AUC / sqrt(N - 1),
      NG = sum(NG, na.rm = TRUE),
      DB =  sum(DB, na.rm = TRUE),
      SM = sum(SM, na.rm = TRUE)
    )

    # Get shift vals
    # if L is 0, that means the no growth on no drug
    # if L is NA at 0, that means the spot was removed due to contamination
    # if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
    # otherwise calculate shift at no drug conc
    if (is.na(X_stats_interaction$mean_L[1]) || X_stats_interaction$mean_L[1] == 0) {
      X_stats_interaction$Raw_Shift_L <- 0
      X_stats_interaction$Raw_Shift_K <- 0
      X_stats_interaction$Raw_Shift_r <- 0
      X_stats_interaction$Raw_Shift_AUC <- 0
      X_stats_interaction$Z_Shift_L <- 0
      X_stats_interaction$Z_Shift_K <- 0
      X_stats_interaction$Z_Shift_r <- 0
      X_stats_interaction$Z_Shift_AUC <- 0
    } else {
      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1] / X_stats_BY_L$sd[1]
      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1] / X_stats_BY_K$sd[1]
      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1] / X_stats_BY_r$sd[1]
      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1] / X_stats_BY_AUC$sd[1]
    }

    # Get WT vals
    X_stats_interaction$WT_l <- X_stats_BY_L$mean
    X_stats_interaction$WT_K <- X_stats_BY_K$mean
    X_stats_interaction$WT_r <- X_stats_BY_r$mean
    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean

    # Get WT SD
    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd

    # Only get scores if there's growth at no drug
    if (X_stats_interaction$mean_L[1] != 0 && !is.na(X_stats_interaction$mean_L[1])) {

      # Calculate expected values
      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC

      # Calculate normalized delta values
      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L
      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
      X_stats_interaction$Delta_r <- X_stats_interaction$mean_r - X_stats_interaction$Exp_r
      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC

      # Disregard shift for no growth values in Z score calculation
      if (sum(X_stats_interaction$NG, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_l
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_K <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_K
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_r <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_r
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_AUC <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_AUC

      }
      # Disregard shift for set to max values in Z score calculation
      if (sum(X_stats_interaction$SM, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_l
        # Only calculate the L value without shift since L is the only adjusted value
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_K <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_K
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_r <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_r
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_AUC <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_AU
      }

      # Calculate Z score at each concentration
      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L) / (X_stats_interaction$WT_sd_l)
      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K) / (X_stats_interaction$WT_sd_K)
      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r) / (X_stats_interaction$WT_sd_r)
      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC) / (X_stats_interaction$WT_sd_AUC)

      # Get linear model
      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor, data = X_stats_interaction)

      # Get interaction score calculated by linear model and R-squared value for the fit
      gene_interaction_L <- MAX_CONC * (gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
      r_squared_l <- summary(gene_lm_L)$r.squared
      gene_interaction_K <- MAX_CONC * (gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
      r_squared_K <- summary(gene_lm_K)$r.squared
      gene_interaction_r <- MAX_CONC * (gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
      r_squared_r <- summary(gene_lm_r)$r.squared
      gene_interaction_AUC <- MAX_CONC * (gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
      r_squared_AUC <- summary(gene_lm_AUC)$r.squared

      # Get total of non removed values
      Num_non_Removed_Conc <- total_conc_nums - sum(X_stats_interaction$DB, na.rm = TRUE) - 1

      # Report the scores
      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_Gene_Sel$OrfRep[1]
      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_Gene_Sel$Gene[1]
      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_L[1]
      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_L[1]
      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        gene_interaction_L
      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        r_squared_l
      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_L, na.rm = TRUE)
      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_L, na.rm = TRUE) / (Num_non_Removed_Conc)
      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_K[1]
      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_K[1]
      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        gene_interaction_K
      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        r_squared_K
      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_K, na.rm = TRUE)
      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_K, na.rm = TRUE) / (Num_non_Removed_Conc)
      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_r[1]
      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_r[1]
      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        gene_interaction_r
      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        r_squared_r
      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_r, na.rm = TRUE)
      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_r, na.rm = TRUE) / (total_conc_nums - 1)
      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_AUC[1]
      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_AUC[1]
      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        gene_interaction_AUC
      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        r_squared_AUC
      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_AUC, na.rm = TRUE)
      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_AUC, na.rm = TRUE) / (total_conc_nums - 1)
    }

    if (X_stats_interaction$mean_L[1] == 0 || is.na(X_stats_interaction$mean_L[1])) {

      # Calculate expected values
      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC

      # Calculate normalized delta values
      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L
      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
      X_stats_interaction$Delta_r <- X_stats_interaction$mean_r - X_stats_interaction$Exp_r
      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC

      # Disregard shift for missing values in Z score calculation
      if (sum(X_stats_interaction$NG, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_l
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_K <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_K
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_r <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_r
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_AUC <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_AUC
      }
      # Disregard shift for set to max values in Z score calculation
      if (sum(X_stats_interaction$SM, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_l
        # Only calculate the L value without shift since L is the only adjusted value
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_K <-
        #  X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_K
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_r <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_r
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_AUC <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_AUC
        
      }
      
      # Calculate Z score at each concentration
      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L) / (X_stats_interaction$WT_sd_l)
      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K) / (X_stats_interaction$WT_sd_K)
      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r) / (X_stats_interaction$WT_sd_r)
      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC) / (X_stats_interaction$WT_sd_AUC)
      
      # NA values for the next part since there's an NA or 0 at the no drug.
      gene_lm_L <- NA
      gene_lm_K <- NA
      gene_lm_r <- NA
      gene_lm_AUC <- NA
      gene_interaction_L <- NA
      r_squared_l <- NA
      gene_interaction_K <- NA
      r_squared_K <- NA
      gene_interaction_r <- NA
      r_squared_r <- NA
      gene_interaction_AUC <- NA
      r_squared_AUC <- NA
      X_stats_interaction$Raw_Shift_L <- NA
      X_stats_interaction$Raw_Shift_K <- NA
      X_stats_interaction$Raw_Shift_r <- NA
      X_stats_interaction$Raw_Shift_AUC <- NA
      X_stats_interaction$Z_Shift_L <- NA
      X_stats_interaction$Z_Shift_K <- NA
      X_stats_interaction$Z_Shift_r <- NA
      X_stats_interaction$Z_Shift_AUC <- NA
      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
    }
    
    if (i == 1) {
      X_stats_interaction_ALL_RF <- X_stats_interaction
    }

    if (i > 1) {
      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF, X_stats_interaction)
    }
    
    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG, na.rm = TRUE)
    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB, na.rm = TRUE)
    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM, na.rm = TRUE)
    
    # X_stats_L_int_temp <- rbind(X_stats_L_int_temp, X_stats_L_int)
  }

  print("Pass RF Calculation loop")
  
  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L, na.rm = TRUE)
  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K, na.rm = TRUE)
  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r, na.rm = TRUE)
  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC, na.rm = TRUE)
  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L, na.rm = TRUE)
  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K, na.rm = TRUE)
  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r, na.rm = TRUE)
  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC, na.rm = TRUE)
  
  print(paste("Mean RF linear regression score L", lm_mean_L))
  
  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L) / (lm_sd_L)
  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K) / (lm_sd_K)
  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r) / (lm_sd_r)
  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC) / (lm_sd_AUC)
  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L, decreasing = TRUE), ]
  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG, decreasing = TRUE), ]
  write.csv(InteractionScores_RF, file.path(outDir, "RF_ZScores_Interaction.csv"), row.names = FALSE)
  
  for (i in 1:num_genes_RF) {
    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel, ]
    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel, ]
  
    p_rf_l[[i]] <- ggplot(X_ZCalculations, aes(Conc_Num_Factor, Delta_L)) +
      geom_point() +
      geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-65, 65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_l), ymax = 0 + (2 * WT_sd_l)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_L, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 35, label = paste("Avg Zscore =", round(X_Int_Scores$Avg_Zscore_L, 2))) +
      annotate("text", x = 1, y = 25, label = paste("lm Zscore =", round(X_Int_Scores$Z_lm_L, 2))) +
      annotate("text", x = 1, y = -25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))
      ) +
      scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) +
      theme_publication()
    
    p_rf_K[[i]] <- ggplot(
      X_ZCalculations, aes(Conc_Num_Factor, Delta_K)) +
      geom_point() +
      geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-65, 65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_K), ymax = 0 + (2 * WT_sd_K)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_K, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 35, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_K, 2))) +
      annotate("text", x = 1, y = 25, label = paste("lm ZScore =", round(X_Int_Scores$Z_lm_L, 2))) +
      annotate("text", x = 1, y = -25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))
      ) +
      scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) +
      theme_publication()
    
    p_rf_r[[i]] <- ggplot(
      X_ZCalculations, aes(Conc_Num_Factor, Delta_r)) +
      geom_point() +
      geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-0.65, 0.65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_r), ymax = 0 + (2 * WT_sd_r)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 0.45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_r, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 0.35, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_r, 2))) +
      annotate("text", x = 1, y = 0.25, label = paste("lm ZScore =", round(X_Int_Scores$Z_lm_r, 2))) +
      annotate("text", x = 1, y = -0.25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -0.35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -0.45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))
      ) +
      scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
      theme_publication()
   
    p_rf_AUC[[i]] <- ggplot(
      X_ZCalculations, aes(Conc_Num_Factor, Delta_AUC)) +
      geom_point() +
      geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-6500, 6500)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_AUC), ymax = 0 + (2 * WT_sd_AUC)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 4500, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_AUC, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 3500, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_AUC, 2))) +
      annotate("text", x = 1, y = 2500, label = paste("lm ZScore =", round(X_Int_Scores$Z_lm_AUC, 2))) +
      annotate("text", x = 1, y = -2500, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -3500, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -4500, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))
      ) +
      scale_y_continuous(breaks = c(-6000, -5000, -4000, -3000, -2000, -1000, 0, 1000, 2000, 3000, 4000, 5000, 6000)) +
      theme_publication()
    
    if (i == 1) {
      X_stats_interaction_ALL_RF_final <- X_ZCalculations
    }
    if (i > 1) {
      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final, X_ZCalculations)
    }
  }
  print("Pass RF ggplot loop")
  write.csv(X_stats_interaction_ALL_RF_final, file.path(outDir, "RF_ZScore_Calculations.csv"), row.names = FALSE)
  
  # Part 5 - Get Zscores for Gene deletion strains
  
  # Get total number of genes for the next loop
  num_genes <- length(unique(X2$OrfRep))
  # print(num_genes)
  
  # Create the output data.frame containing columns for each deletion strain
  InteractionScores <- unique(X2["OrfRep"])
  # InteractionScores$Gene <- unique(X2$Gene)
  InteractionScores$Gene <- NA
  InteractionScores$Raw_Shift_L <- NA
  InteractionScores$Z_Shift_L <- NA
  InteractionScores$lm_Score_L <- NA
  InteractionScores$Z_lm_L <- NA
  InteractionScores$R_Squared_L <- NA
  InteractionScores$Sum_Z_Score_L <- NA
  InteractionScores$Avg_Zscore_L <- NA
  InteractionScores$Raw_Shift_K <- NA
  InteractionScores$Z_Shift_K <- NA
  InteractionScores$lm_Score_K <- NA
  InteractionScores$Z_lm_K <- NA
  InteractionScores$R_Squared_K <- NA
  InteractionScores$Sum_Z_Score_K <- NA
  InteractionScores$Avg_Zscore_K <- NA
  InteractionScores$Raw_Shift_r <- NA
  InteractionScores$Z_Shift_r <- NA
  InteractionScores$lm_Score_r <- NA
  InteractionScores$Z_lm_r <- NA
  InteractionScores$R_Squared_r <- NA
  InteractionScores$Sum_Z_Score_r <- NA
  InteractionScores$Avg_Zscore_r <- NA
  InteractionScores$Raw_Shift_AUC <- NA
  InteractionScores$Z_Shift_AUC <- NA
  InteractionScores$lm_Score_AUC <- NA
  InteractionScores$Z_lm_AUC <- NA
  InteractionScores$R_Squared_AUC <- NA
  InteractionScores$Sum_Z_Score_AUC <- NA
  InteractionScores$Avg_Zscore_AUC <- NA
  InteractionScores$NG <- NA
  InteractionScores$DB <- NA
  InteractionScores$SM <- NA
  
  for (i in 1:num_genes) {
    # Get each deletion strain ORF
    Gene_Sel <- unique(X2$OrfRep)[i]
    # Extract only the current deletion strain and its data
    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel, ]
    
    X_stats_interaction <- ddply(
      X_Gene_Sel,
      c("OrfRep", "Gene", "Conc_Num", "Conc_Num_Factor"),
      summarise,
      N = (length(l)),
      mean_L = mean(l, na.rm = TRUE),
      median_L = median(l, na.rm = TRUE),
      sd_L = sd(l, na.rm = TRUE),
      se_L = sd_L / sqrt(N - 1),
      mean_K = mean(K, na.rm = TRUE),
      median_K = median(K, na.rm = TRUE),
      sd_K = sd(K, na.rm = TRUE),
      se_K = sd_K / sqrt(N - 1),
      mean_r = mean(r, na.rm = TRUE),
      median_r = median(r, na.rm = TRUE),
      sd_r = sd(r, na.rm = TRUE),
      se_r = sd_r / sqrt(N - 1),
      mean_AUC = mean(AUC, na.rm = TRUE),
      median_AUC = median(AUC, na.rm = TRUE),
      sd_AUC = sd(AUC, na.rm = TRUE),
      se_AUC = sd_AUC / sqrt(N - 1),
      NG = sum(NG, na.rm = TRUE),
      DB = sum(DB, na.rm = TRUE),
      SM = sum(SM, na.rm = TRUE)
    )
    
    # Get shift vals
    # if L is 0, that means the no growth on no drug
    # if L is NA at 0, that means the spot was removed due to contamination
    # if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
    # otherwise calculate shift at no drug conc
    if (is.na(X_stats_interaction$mean_L[1]) || X_stats_interaction$mean_L[1] == 0) {
      X_stats_interaction$Raw_Shift_L <- 0
      X_stats_interaction$Raw_Shift_K <- 0
      X_stats_interaction$Raw_Shift_r <- 0
      X_stats_interaction$Raw_Shift_AUC <- 0
      X_stats_interaction$Z_Shift_L <- 0
      X_stats_interaction$Z_Shift_K <- 0
      X_stats_interaction$Z_Shift_r <- 0
      X_stats_interaction$Z_Shift_AUC <- 0
    } else {
      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1] / X_stats_BY_L$sd[1]
      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1] / X_stats_BY_K$sd[1]
      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1] / X_stats_BY_r$sd[1]
      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1] / X_stats_BY_AUC$sd[1]
    }
    
    # Get WT vals
    X_stats_interaction$WT_l <- X_stats_BY_L$mean
    X_stats_interaction$WT_K <- X_stats_BY_K$mean
    X_stats_interaction$WT_r <- X_stats_BY_r$mean
    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
    
    # Get WT SD
    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
    
    # Only get scores if there's growth at no drug
    if (X_stats_interaction$mean_L[1] != 0 && !is.na(X_stats_interaction$mean_L[1])) {
      
      # Calculate expected values
      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
      
      # Calculate normalized delta values
      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L
      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
      X_stats_interaction$Delta_r <- X_stats_interaction$mean_r - X_stats_interaction$Exp_r
      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
      
      # Disregard shift for no growth values in Z score calculation
      if (sum(X_stats_interaction$NG, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_l
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_K <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_K
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_r <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_r
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_AUC <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_AUC
      }

      # Disregard shift for set to max values in Z score calculation
      if (sum(X_stats_interaction$SM, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_l
        # Only calculate the L value without shift since L is the only adjusted value
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_K <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_K
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_r <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_r
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_AUC <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_AUC
      }
      
      # Calculate Z score at each concentration
      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L) / (X_stats_interaction$WT_sd_l)
      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K) / (X_stats_interaction$WT_sd_K)
      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r) / (X_stats_interaction$WT_sd_r)
      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC) / (X_stats_interaction$WT_sd_AUC)
      
      # Get linear model
      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor, data = X_stats_interaction)
      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor, data = X_stats_interaction)
      
      # Get interaction score calculated by linear model and R-squared value for the fit
      gene_interaction_L <- MAX_CONC * (gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
      r_squared_l <- summary(gene_lm_L)$r.squared
      gene_interaction_K <- MAX_CONC * (gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
      r_squared_K <- summary(gene_lm_K)$r.squared
      gene_interaction_r <- MAX_CONC * (gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
      r_squared_r <- summary(gene_lm_r)$r.squared
      gene_interaction_AUC <- MAX_CONC * (gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
      
      # Get total of non removed values
      Num_non_Removed_Conc <- total_conc_nums - sum(X_stats_interaction$DB, na.rm = TRUE) - 1
      
      # Report the scores
      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <-
        as.character(X_Gene_Sel$OrfRep[1])
      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <-
        as.character(X_Gene_Sel$Gene[1])
      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_L[1]
      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_L[1]
      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <-
        gene_interaction_L
      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <-
        (gene_interaction_L - lm_mean_L) / lm_sd_L
      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <-
        r_squared_l
      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_L, na.rm = TRUE)
      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_L, na.rm = TRUE) / (Num_non_Removed_Conc)
      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_K[1]
      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_K[1]
      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <-
        gene_interaction_K
      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-
        (gene_interaction_K - lm_mean_K) / lm_sd_K
      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <-
        r_squared_K
      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_K, na.rm = TRUE)
      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_K, na.rm = TRUE) / (Num_non_Removed_Conc)
      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_r[1]
      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_r[1]
      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <-
        gene_interaction_r
      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <-
        (gene_interaction_r - lm_mean_r) / lm_sd_r
      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <-
        r_squared_r
      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_r, na.rm = TRUE)
      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_r, na.rm = TRUE) / (total_conc_nums - 1)
      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Raw_Shift_AUC[1]
      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        X_stats_interaction$Z_Shift_AUC[1]
      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        gene_interaction_AUC
      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        (gene_interaction_AUC - lm_mean_AUC) / lm_sd_AUC
      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        r_squared_AUC
      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_AUC, na.rm = TRUE)
      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <-
        sum(X_stats_interaction$Zscore_AUC, na.rm = TRUE) / (total_conc_nums - 1)
    }
    
    if (X_stats_interaction$mean_L[1] == 0 || is.na(X_stats_interaction$mean_L[1])) {
      
      # Calculate expected values
      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
      
      # Calculate normalized delta values
      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L
      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
      X_stats_interaction$Delta_r <- X_stats_interaction$mean_r - X_stats_interaction$Exp_r
      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
      
      # Disregard shift for missing values in Z score calculatiom
      if (sum(X_stats_interaction$NG, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_l
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_K <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_K
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_r <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_r
        X_stats_interaction[X_stats_interaction$NG == 1, ]$Delta_AUC <-
          X_stats_interaction[X_stats_interaction$NG == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1, ]$WT_AUC
      }

      # Disregard shift for set to max values in Z score calculation
      if (sum(X_stats_interaction$SM, na.rm = TRUE) > 0) {
        X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_L <-
          X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_l
        
        # Only calculate the L value without shift since L is the only adjusted value
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_K <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_K
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_r <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_r
        # X_stats_interaction[X_stats_interaction$SM == 1, ]$Delta_AUC <-
        #   X_stats_interaction[X_stats_interaction$SM == 1, ]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1, ]$WT_AUC
      }
      
      # Calculate Z score at each concentration
      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L) / (X_stats_interaction$WT_sd_l)
      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K) / (X_stats_interaction$WT_sd_K)
      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r) / (X_stats_interaction$WT_sd_r)
      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC) / (X_stats_interaction$WT_sd_AUC)
      
      # NA values for the next part since there's an NA or 0 at the no drug.
      gene_lm_L <- NA
      gene_lm_K <- NA
      gene_lm_r <- NA
      gene_interaction_L <- NA
      r_squared_l <- NA
      gene_interaction_K <- NA
      r_squared_K <- NA
      gene_interaction_r <- NA
      r_squared_r <- NA
      X_stats_interaction$Raw_Shift_L <- NA
      X_stats_interaction$Raw_Shift_K <- NA
      X_stats_interaction$Raw_Shift_r <- NA
      X_stats_interaction$Raw_Shift_AUC <- NA
      X_stats_interaction$Z_Shift_L <- NA
      X_stats_interaction$Z_Shift_K <- NA
      X_stats_interaction$Z_Shift_r <- NA
      X_stats_interaction$Z_Shift_AUC <- NA
      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
    }
    
    if (i == 1) {
      X_stats_interaction_ALL <- X_stats_interaction
    }
    if (i > 1) {
      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL, X_stats_interaction)
    }
    
    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG, na.rm = TRUE)
    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB, na.rm = TRUE)
    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM, na.rm = TRUE)
    
    # X_stats_L_int_temp <- rbind(X_stats_L_int_temp, X_stats_L_int)
  }

  print("Pass Int Calculation loop")
  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L, decreasing = TRUE), ]
  InteractionScores <- InteractionScores[order(InteractionScores$NG, decreasing = TRUE), ]
  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
  # X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG, decreasing = TRUE), ]
  write.csv(InteractionScores, file.path(outDir, "ZScores_Interaction.csv"), row.names = FALSE)
  
  InteractionScores_deletion_enhancers_L <-
    InteractionScores[InteractionScores$Avg_Zscore_L >= 2, ]
  InteractionScores_deletion_enhancers_K <-
    InteractionScores[InteractionScores$Avg_Zscore_K <= -2, ]
  InteractionScores_deletion_suppressors_L <-
    InteractionScores[InteractionScores$Avg_Zscore_L <= -2, ]
  InteractionScores_deletion_suppressors_K <-
    InteractionScores[InteractionScores$Avg_Zscore_K >= 2, ]
  InteractionScores_deletion_enhancers_and_Suppressors_L <-
    InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2, ]
  InteractionScores_deletion_enhancers_and_Suppressors_K <-
    InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2, ]
  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <-
    InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2, ]
  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <-
    InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2, ]
  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <-
    InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2, ]
  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <-
    InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2, ]
  InteractionScores_deletion_enhancers_L <-
    InteractionScores_deletion_enhancers_L[
      !is.na(InteractionScores_deletion_enhancers_L$OrfRep), ]
  InteractionScores_deletion_enhancers_K <-
    InteractionScores_deletion_enhancers_K[
      !is.na(InteractionScores_deletion_enhancers_K$OrfRep), ]
  InteractionScores_deletion_suppressors_L <-
    InteractionScores_deletion_suppressors_L[
      !is.na(InteractionScores_deletion_suppressors_L$OrfRep), ]
  InteractionScores_deletion_suppressors_K <-
    InteractionScores_deletion_suppressors_K[
      !is.na(InteractionScores_deletion_suppressors_K$OrfRep), ]
  InteractionScores_deletion_enhancers_and_Suppressors_L <-
    InteractionScores_deletion_enhancers_and_Suppressors_L[
      !is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep), ]
  InteractionScores_deletion_enhancers_and_Suppressors_K <-
    InteractionScores_deletion_enhancers_and_Suppressors_K[
      !is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep), ]
  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <-
    InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[
      !is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep), ]
  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <-
    InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[
      !is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep), ]
  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <-
    InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[
      !is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep), ]
  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <-
    InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[
      !is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep), ]
  write.csv(InteractionScores_deletion_enhancers_L,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_K,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_suppressors_L,
    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_suppressors_K,
    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,
    file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,
    file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,
    file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,
    file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv"), row.names = FALSE)
  
  # Get enhancers and suppressors for linear regression
  InteractionScores_deletion_enhancers_L_lm <-
    InteractionScores[InteractionScores$Z_lm_L >= 2, ]
  InteractionScores_deletion_enhancers_K_lm <-
    InteractionScores[InteractionScores$Z_lm_K <= -2, ]
  InteractionScores_deletion_suppressors_L_lm <-
    InteractionScores[InteractionScores$Z_lm_L <= -2, ]
  InteractionScores_deletion_suppressors_K_lm <-
    InteractionScores[InteractionScores$Z_lm_K >= 2, ]
  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <-
    InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2, ]
  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <-
    InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2, ]
  InteractionScores_deletion_enhancers_L_lm <-
    InteractionScores_deletion_enhancers_L_lm[
      !is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep), ]
  InteractionScores_deletion_enhancers_K_lm <-
    InteractionScores_deletion_enhancers_K_lm[
      !is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep), ]
  InteractionScores_deletion_suppressors_L_lm <-
    InteractionScores_deletion_suppressors_L_lm[
      !is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep), ]
  InteractionScores_deletion_suppressors_K_lm <-
    InteractionScores_deletion_suppressors_K_lm[
      !is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep), ]
  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <-
    InteractionScores_deletion_enhancers_and_Suppressors_L_lm[
      !is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep), ]
  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <-
    InteractionScores_deletion_enhancers_and_Suppressors_K_lm[
      !is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep), ]
  
  write.csv(InteractionScores_deletion_enhancers_L_lm,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_K_lm,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_suppressors_L_lm,
    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_suppressors_K_lm,
    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv"), row.names = FALSE)
  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,
    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv"), row.names = FALSE)
  # write.csv(Labels, studyInfo, row.names = FALSE)
  # write.table(Labels, file.path("../Code/StudyInfo.txt"), sep = "\t", row.names = FALSE)
  
  for (i in 1:num_genes) {
    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel, ]
    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel, ]
  
    p_l[[i]] <- ggplot(X_ZCalculations, aes(Conc_Num_Factor, Delta_L)) +
      geom_point() + geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-65, 65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_l), ymax = 0 + (2 * WT_sd_l)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_L, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 35, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_L, 2))) +
      annotate("text", x = 1, y = 25, label = paste("Z lm Score =", round(X_Int_Scores$Z_lm_L, 2))) +
      annotate("text", x = 1, y = -25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))) +
      scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) +
      theme_publication()
    
    p_K[[i]] <- ggplot(X_ZCalculations, aes(Conc_Num_Factor, Delta_K)) +
      geom_point() + geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-65, 65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_K), ymax = 0 + (2 * WT_sd_K)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_K, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 35, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_K, 2))) +
      annotate("text", x = 1, y = 25, label = paste("Z lm Score =", round(X_Int_Scores$Z_lm_K, 2))) +
      annotate("text", x = 1, y = -25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))) +
      scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) +
      theme_publication()
    
    p_r[[i]] <- ggplot(X_ZCalculations, aes(Conc_Num_Factor, Delta_r)) +
      geom_point() + geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-0.65, 0.65)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_r), ymax = 0 + (2 * WT_sd_r)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 0.45, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_r, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 0.35, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_r, 2))) +
      annotate("text", x = 1, y = 0.25, label = paste("Z lm Score =", round(X_Int_Scores$Z_lm_r, 2))) +
      annotate("text", x = 1, y = -0.25, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -0.35, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -0.45, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))) +
      scale_y_continuous(breaks = c(-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6)) +
      theme_publication()
    
    p_AUC[[i]] <- ggplot(X_ZCalculations, aes(Conc_Num_Factor, Delta_AUC)) +
      geom_point() + geom_smooth(method = "lm", formula = y ~ x, se = FALSE) +
      coord_cartesian(ylim = c(-6500, 6500)) +
      geom_errorbar(aes(ymin = 0 - (2 * WT_sd_AUC), ymax = 0 + (2 * WT_sd_AUC)), alpha = 0.3) +
      ggtitle(paste(X_ZCalculations$OrfRep[1], X_ZCalculations$Gene[1], sep = "      ")) +
      annotate("text", x = 1, y = 4500, label = paste("ZShift =", round(X_Int_Scores$Z_Shift_AUC, 2))) +
      scale_color_discrete(guide = FALSE) +
      # annotate("text", x = 1, y = 3500, label = paste("Avg ZScore =", round(X_Int_Scores$Avg_Zscore_AUC, 2))) +
      annotate("text", x = 1, y = 2500, label = paste("Z lm Score =", round(X_Int_Scores$Z_lm_AUC, 2))) +
      annotate("text", x = 1, y = -2500, label = paste("NG =", X_Int_Scores$NG)) +
      annotate("text", x = 1, y = -3500, label = paste("DB =", X_Int_Scores$DB)) +
      annotate("text", x = 1, y = -4500, label = paste("SM =", X_Int_Scores$SM)) +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X_ZCalculations$Conc_Num_Factor),
        labels = unique(as.character(X_ZCalculations$Conc_Num))) +
      scale_y_continuous(breaks = c(-6000, -5000, -4000, -3000, -2000, -1000, 0, 1000, 2000, 3000, 4000, 5000, 6000)) +
      theme_publication()
    
    if (i == 1) {
      X_stats_interaction_ALL_final <- X_ZCalculations
    }
    if (i > 1) {
      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final, X_ZCalculations)
    }
  }
  print("Pass Int ggplot loop")
  write.csv(X_stats_interaction_ALL_final, file.path(outDir, "ZScore_Calculations.csv"), row.names = FALSE)
  
  Blank <- ggplot(X2_RF) + geom_blank()
  
  pdf(file.path(outDir, "InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)
  
  X_stats_X2_RF <- ddply(
    X2_RF,
    c("Conc_Num", "Conc_Num_Factor"),
    summarise,
    mean_L = mean(l, na.rm = TRUE),
    median_L = median(l, na.rm = TRUE),
    max_L = max(l, na.rm = TRUE),
    min_L = min(l, na.rm = TRUE),
    sd_L = sd(l, na.rm = TRUE),
    mean_K = mean(K, na.rm = TRUE),
    median_K = median(K, na.rm = TRUE),
    max_K = max(K, na.rm = TRUE),
    min_K = min(K, na.rm = TRUE),
    sd_K = sd(K, na.rm = TRUE),
    mean_r = mean(r, na.rm = TRUE),
    median_r = median(r, na.rm = TRUE),
    max_r = max(r, na.rm = TRUE),
    min_r = min(r, na.rm = TRUE),
    sd_r = sd(r, na.rm = TRUE),
    mean_AUC = mean(AUC, na.rm = TRUE),
    median_AUC = median(AUC, na.rm = TRUE),
    max_AUC = max(AUC, na.rm = TRUE),
    min_AUC = min(AUC, na.rm = TRUE),
    sd_AUC = sd(AUC, na.rm = TRUE),
    NG = sum(NG, na.rm = TRUE),
    DB = sum(DB, na.rm = TRUE),
    SM = sum(SM, na.rm = TRUE)
  )
  
  L_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, l)) + geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red") +
      stat_summary(fun = mean, geom = "point", color = "red") +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X2_RF$Conc_Num_Factor),
        labels = as.character(unique(X2_RF$Conc_Num))
      ) +
      ggtitle(paste(s, "Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim = c(0, 160)) +
      annotate("text", x = -0.25, y = 10, label = "NG") +
      annotate("text", x = -0.25, y = 5, label = "DB") +
      annotate("text", x = -0.25, y = 0, label = "SM") +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 10, label = X_stats_X2_RF$NG) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 5, label = X_stats_X2_RF$DB) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 0, label = X_stats_X2_RF$SM) +
      theme_publication()
  
  K_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, K)) + geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red") +
      stat_summary(fun = mean, geom = "point", color = "red") +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X2_RF$Conc_Num_Factor),
        labels = as.character(unique(X2_RF$Conc_Num))
      ) +
      ggtitle(paste(s, "Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim = c(-20, 160)) +
      annotate("text", x = -0.25, y = -5, label = "NG") +
      annotate("text", x = -0.25, y = -12.5, label = "DB") +
      annotate("text", x = -0.25, y = -20, label = "SM") +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -5, label = X_stats_X2_RF$NG) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -12.5, label = X_stats_X2_RF$DB) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -20, label = X_stats_X2_RF$SM) +
      theme_publication()
  
  R_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, r)) + geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red") +
      stat_summary(fun = mean, geom = "point", color = "red") +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X2_RF$Conc_Num_Factor),
        labels = as.character(unique(X2_RF$Conc_Num))
      ) +
      ggtitle(paste(s, "Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim = c(0, 1)) +
      annotate("text", x = -0.25, y = .9, label = "NG") +
      annotate("text", x = -0.25, y = .8, label = "DB") +
      annotate("text", x = -0.25, y = .7, label = "SM") +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .9, label = X_stats_X2_RF$NG) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .8, label = X_stats_X2_RF$DB) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .7, label = X_stats_X2_RF$SM) +
      theme_publication()
  
  AUC_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, AUC)) + geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red") +
      stat_summary(fun = mean, geom = "point", color = "red") +
      scale_x_continuous(
        name = unique(X$Drug[1]),
        breaks = unique(X2_RF$Conc_Num_Factor),
        labels = as.character(unique(X2_RF$Conc_Num))
      ) +
      ggtitle(paste(s, "Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim = c(0, 12500)) +
      annotate("text", x = -0.25, y = 11000, label = "NG") +
      annotate("text", x = -0.25, y = 10000, label = "DB") +
      annotate("text", x = -0.25, y = 9000, label = "SM") +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 11000, label = X_stats_X2_RF$NG) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 10000, label = X_stats_X2_RF$DB) +
      annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 9000, label = X_stats_X2_RF$SM) +
      theme_publication()
      
  L_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), l)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for L with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 160)) +
    theme_publication()
  
  K_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), K)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for K with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 130)) +
    theme_publication()
  
  r_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), r)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for r with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 1)) +
    theme_publication()
  
  AUC_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), AUC)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for AUC with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 12500)) +
    theme_publication()
  
  grid.arrange(L_Stats, K_Stats, R_Stats, AUC_Stats, ncol = 2, nrow = 2)
  grid.arrange(L_Stats_Box, K_Stats_Box, r_Stats_Box, AUC_Stats_Box, ncol = 2, nrow = 2)
  
  # Plot the references
  # grid.arrange(p3, p3_K, p3_r, p4, p4_K, p4_r, p5, p5_K, p5_r, p6, p6_K, p6_r, ncol = 3, nrow = 4)
  # grid.arrange(p5, p5_K, p5_r, p6, p6_K, p6_r, ncol = 3, nrow = 2)
  
  # Loop for grid arrange 4x3
  j <- rep(1, ((num_genes) / 3) - 1)
  for (n in 1:length(j)) {
    j[n + 1] <- n * 3 + 1
  }

  # Loop for printing each plot
  num <- 0
  for (m in 1:(round((num_genes) / 3) - 1)) {
    num <- j[m]
    grid.arrange(p_l[[num]], p_K[[num]], p_r[[num]], p_AUC[[num]],
    p_l[[num + 1]], p_K[[num + 1]], p_r[[num + 1]], p_AUC[[num + 1]],
    p_l[[num + 2]], p_K[[num + 2]], p_r[[num + 2]], p_AUC[[num + 2]], ncol = 4, nrow = 3)
    # grid.arrange(p_l[[364]], p_K[[364]], p_r[[364]],
    # p_l[[365]], p_K[[365]], p_r[[365]], p_l[[366]], p_K[[366]], p_r[[366]], ncol = 3, nrow = 3)
    # p1[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p1[[num + 4]], p_K[[num + 4]], p_r[[num + 4]]
  }

  if (num_genes != (num + 2)) {
    total_num <- num_genes - (num  +  2)
    if (total_num == 5) {
      grid.arrange(p_l[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p_AUC[[num + 3]],
        p_l[[num + 4]], p_K[[num + 4]], p_r[[num + 4]], p_AUC[[num + 4]],
        p_l[[num + 5]], p_K[[num + 5]], p_r[[num + 5]], p_AUC[[num + 5]], ncol = 4, nrow = 3)

      grid.arrange(p_l[[num + 6]], p_K[[num + 6]], p_r[[num + 6]], p_AUC[[num + 6]],
        p_l[[num + 7]], p_K[[num + 7]], p_r[[num + 7]], p_AUC[[num + 7]],
        Blank, Blank, Blank, Blank, ncol = 4, nrow = 3)
    }
    if (total_num == 4) {
      grid.arrange(p_l[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p_AUC[[num + 3]],
        p_l[[num + 4]], p_K[[num + 4]], p_r[[num + 4]], p_AUC[[num + 4]],
        p_l[[num + 5]], p_K[[num + 5]], p_r[[num + 5]], p_AUC[[num + 5]], ncol = 4, nrow = 3)

      grid.arrange(p_l[[num + 6]], p_K[[num + 6]], p_r[[num + 6]], p_AUC[[num + 6]],
        Blank, Blank, Blank, Blank, Blank, Blank, Blank, Blank, ncol = 4, nrow = 3)
    }
    if (total_num == 3) {
      grid.arrange(p_l[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p_AUC[[num + 3]],
        p_l[[num + 4]], p_K[[num + 4]], p_r[[num + 4]], p_AUC[[num + 4]],
        p_l[[num + 5]], p_K[[num + 5]], p_r[[num + 5]], p_AUC[[num + 5]], ncol = 4, nrow = 3)
      # grid.arrange(p_l[[num + 6]], p_K[[num + 6]], p_r[[num + 6]],
      # p_l[[num + 7]], p_K[[num + 7]], p_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
    if (total_num == 2) {
      grid.arrange(p_l[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p_AUC[[num + 3]],
        p_l[[num + 4]], p_K[[num + 4]], p_r[[num + 4]], p_AUC[[num + 4]],
        Blank, Blank, Blank, Blank, ncol = 4, nrow = 3)
      # grid.arrange(p_l[[num + 6]], p_K[[num + 6]], p_r[[num + 6]],
      # p_l[[num + 7]], p_K[[num + 7]], p_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
    if (total_num == 1) {
      grid.arrange(p_l[[num + 3]], p_K[[num + 3]], p_r[[num + 3]], p_AUC[[num + 3]],
        Blank, Blank, Blank, Blank, Blank, Blank, Blank, Blank, ncol = 4, nrow = 3)
      # grid.arrange(p_l[[num + 6]], p_K[[num + 6]], p_r[[num + 6]],
      # p_l[[num + 7]], p_K[[num + 7]], p_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
  }

  dev.off()

  pdf(file.path(outDir, "RF_InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)

  X_stats_X2_RF <- ddply(
    X2_RF,
    c("Conc_Num", "Conc_Num_Factor"),
    summarise,
    mean_L = mean(l, na.rm = TRUE),
    median_L = median(l, na.rm = TRUE),
    max_L = max(l, na.rm = TRUE),
    min_L = min(l, na.rm = TRUE),
    sd_L = sd(l, na.rm = TRUE),
    mean_K = mean(K, na.rm = TRUE),
    median_K = median(K, na.rm = TRUE),
    max_K = max(K, na.rm = TRUE),
    min_K = min(K, na.rm = TRUE),
    sd_K = sd(K, na.rm = TRUE),
    mean_r = mean(r, na.rm = TRUE),
    median_r = median(r, na.rm = TRUE),
    max_r = max(r, na.rm = TRUE),
    min_r = min(r, na.rm = TRUE),
    sd_r = sd(r, na.rm = TRUE),
    mean_AUC = mean(AUC, na.rm = TRUE),
    median_AUC = median(AUC, na.rm = TRUE),
    max_AUC = max(AUC, na.rm = TRUE),
    min_AUC = min(AUC, na.rm = TRUE),
    sd_AUC = sd(AUC, na.rm = TRUE),
    NG = sum(NG, na.rm = TRUE),
    DB = sum(DB, na.rm = TRUE),
    SM = sum(SM, na.rm = TRUE)
  )
  
  L_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, l)) +
    geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red"
    ) +
    stat_summary(fun = mean, geom = "point", color = "red") +
    scale_x_continuous(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for L with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 130)) +
    annotate("text", x = -0.25, y = 10, label = "NG") +
    annotate("text", x = -0.25, y = 5, label = "DB") +
    annotate("text", x = -0.25, y = 0, label = "SM") +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 10, label = X_stats_X2_RF$NG) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 5, label = X_stats_X2_RF$DB) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 0, label = X_stats_X2_RF$SM) +
    theme_publication()
  
  K_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, K)) +
    geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red"
    ) +
    stat_summary(fun = mean, geom = "point", color = "red") +
    scale_x_continuous(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for K with SD", sep = " ")) +
    coord_cartesian(ylim = c(-20, 160)) +
    annotate("text", x = -0.25, y = -5, label = "NG") +
    annotate("text", x = -0.25, y = -12.5, label = "DB") +
    annotate("text", x = -0.25, y = -20, label = "SM") +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -5, label = X_stats_X2_RF$NG) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -12.5, label = X_stats_X2_RF$DB) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = -20, label = X_stats_X2_RF$SM) +
    theme_publication()
  
  R_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, r)) +
    geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red"
    ) +
    stat_summary(fun = mean, geom = "point", color = "red") +
    scale_x_continuous(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for r with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 1)) +
    annotate("text", x = -0.25, y = .9, label = "NG") +
    annotate("text", x = -0.25, y = .8, label = "DB") +
    annotate("text", x = -0.25, y = .7, label = "SM") +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .9, label = X_stats_X2_RF$NG) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .8, label = X_stats_X2_RF$DB) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = .7, label = X_stats_X2_RF$SM) +
    theme_publication()
  
  AUC_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, AUC)) +
    geom_point(position = "jitter", size = 1) +
    stat_summary(
      fun = mean,
      fun.min = function(x) mean(x) - sd(x),
      fun.max = function(x) mean(x) + sd(x),
      geom = "errorbar", color = "red"
    ) +
    stat_summary(fun = mean, geom = "point", color = "red") +
    scale_x_continuous(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for AUC with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 12500)) +
    annotate("text", x = -0.25, y = 11000, label = "NG") +
    annotate("text", x = -0.25, y = 10000, label = "DB") +
    annotate("text", x = -0.25, y = 9000, label = "SM") +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 11000, label = X_stats_X2_RF$NG) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 10000, label = X_stats_X2_RF$DB) +
    annotate("text", x = c(unique(X2_RF$Conc_Num_Factor)), y = 9000, label = X_stats_X2_RF$SM) +
    theme_publication()
  
  L_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), l)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for L with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 130)) +
    theme_publication()
  
  K_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), K)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for K with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 160)) +
    theme_publication()
  
  r_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), r)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for r with SD", sep = " ")) +
    coord_cartesian(ylim = c(0, 1)) +
    theme_publication()
  
  AUC_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), AUC)) +
    geom_boxplot() +
    scale_x_discrete(
      name = unique(X$Drug[1]),
      breaks = unique(X2_RF$Conc_Num_Factor),
      labels = as.character(unique(X2_RF$Conc_Num))
    ) +
    ggtitle(paste(s, "Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim = c(12000, 0)) +
    theme_publication()
  
  grid.arrange(L_Stats, K_Stats, R_Stats, AUC_Stats, ncol = 2, nrow = 2)
  grid.arrange(L_Stats_Box, K_Stats_Box, r_Stats_Box, AUC_Stats_Box, ncol = 2, nrow = 2)
  
  # Plot the references
  # grid.arrange(p3, p3_K, p3_r, p4, p4_K, p4_r, p5, p5_K, p5_r, p6, p6_K, p6_r, ncol = 3, nrow = 4)
  # grid.arrange(p5, p5_K, p5_r, p6, p6_K, p6_r, ncol = 3, nrow = 2)
  
  # Loop for grid arrange 4x3
  j <- rep(1, ((num_genes_RF) / 3) - 1)
  for (n in 1:length(j)) {
    j[n + 1] <- n * 3 + 1
  }

  # Loop for printing each plot
  num <- 0
  for (m in 1:(round((num_genes_RF) / 3) - 1)) {
    num <- j[m]
    grid.arrange(p_rf_l[[num]], p_rf_K[[num]], p_rf_r[[num]], p_rf_AUC[[num]],
      p_rf_l[[num + 1]], p_rf_K[[num + 1]], p_rf_r[[num + 1]], p_rf_AUC[[num + 1]],
      p_rf_l[[num + 2]], p_rf_K[[num + 2]], p_rf_r[[num + 2]], p_rf_AUC[[num + 2]], ncol = 4, nrow = 3)
    # grid.arrange(p_rf_l[[364]], p_rf_K[[364]], p_rf_r[[364]],
    # p_rf_l[[365]], p_rf_K[[365]], p_rf_r[[365]], p_rf_l[[366]], p_rf_K[[366]], p_rf_r[[366]], ncol = 3, nrow = 3)
    # p1[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p1[[num + 4]], p_rf_K[[num + 4]], p_rf_r[[num + 4]]
  }
  if (num_genes_RF != (num + 2)) {
    total_num <- num_genes_RF - (num + 2)

    if (total_num == 5) {
      grid.arrange(p_rf_l[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p_rf_AUC[[num + 3]],
        p_rf_l[[num + 4]], p_rf_K[[num + 4]], p_rf_r[[num + 4]], p_rf_AUC[[num + 4]],
        p_rf_l[[num + 5]], p_rf_K[[num + 5]], p_rf_r[[num + 5]], p_rf_AUC[[num + 5]], ncol = 4, nrow = 3
      )
      grid.arrange(p_rf_l[[num + 6]], p_rf_K[[num + 6]], p_rf_r[[num + 6]], p_rf_AUC[[num + 6]],
        p_rf_l[[num + 7]], p_rf_K[[num + 7]], p_rf_r[[num + 7]], p_rf_AUC[[num + 7]],
        Blank, Blank, Blank, Blank, ncol = 4, nrow = 3
      )
    }

    if (total_num == 4) {
      grid.arrange(p_rf_l[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p_rf_AUC[[num + 3]],
        p_rf_l[[num + 4]], p_rf_K[[num + 4]], p_rf_r[[num + 4]], p_rf_AUC[[num + 4]],
        p_rf_l[[num + 5]], p_rf_K[[num + 5]], p_rf_r[[num + 5]], p_rf_AUC[[num + 5]], ncol = 4, nrow = 3
      )
      grid.arrange(p_rf_l[[num + 6]], p_rf_K[[num + 6]], p_rf_r[[num + 6]], p_rf_AUC[[num + 6]],
        Blank, Blank, Blank, Blank, Blank, Blank, Blank, Blank, ncol = 4, nrow = 3
      )
    }
    
    if (total_num == 3) {
      grid.arrange(p_rf_l[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p_rf_AUC[[num + 3]],
        p_rf_l[[num + 4]], p_rf_K[[num + 4]], p_rf_r[[num + 4]], p_rf_AUC[[num + 4]],
        p_rf_l[[num + 5]], p_rf_K[[num + 5]], p_rf_r[[num + 5]], p_rf_AUC[[num + 5]], ncol = 4, nrow = 3
      )
      # grid.arrange(p_rf_l[[num + 6]], p_rf_K[[num + 6]], p_rf_r[[num + 6]],
      # p_rf_l[[num + 7]], p_rf_K[[num + 7]], p_rf_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
    
    if (total_num == 2) {
      grid.arrange(p_rf_l[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p_rf_AUC[[num + 3]],
        p_rf_l[[num + 4]], p_rf_K[[num + 4]], p_rf_r[[num + 4]], p_rf_AUC[[num + 4]],
        Blank, Blank, Blank, Blank, ncol = 4, nrow = 3
      )
      # grid.arrange(p_rf_l[[num + 6]], p_rf_K[[num + 6]], p_rf_r[[num + 6]],
      # p_rf_l[[num + 7]], p_rf_K[[num + 7]], p_rf_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
    
    if (total_num == 1) {
      grid.arrange(p_rf_l[[num + 3]], p_rf_K[[num + 3]], p_rf_r[[num + 3]], p_rf_AUC[[num + 3]],
      Blank, Blank, Blank, Blank, Blank, Blank, Blank, Blank, ncol = 4, nrow = 3
      )
      # grid.arrange(p_rf_l[[num + 6]], p_rf_K[[num + 6]], p_rf_r[[num + 6]],
      # p_rf_l[[num + 7]], p_rf_K[[num + 7]], p_rf_r[[num + 7]], Blank, Blank, Blank, ncol = 3, nrow = 3)
    }
  }

  dev.off()
  
  # Print rank plots for L and K gene interactions
  InteractionScores_AdjustMissing <- InteractionScores
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L), ]$Avg_Zscore_L <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K), ]$Avg_Zscore_K <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r), ]$Avg_Zscore_r <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC), ]$Avg_Zscore_AUC <- 0.001
  
  InteractionScores_AdjustMissing$L_Rank <- NA
  InteractionScores_AdjustMissing$K_Rank <- NA
  InteractionScores_AdjustMissing$r_Rank <- NA
  InteractionScores_AdjustMissing$AUC_Rank <- NA
  
  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
  
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L), ]$Z_lm_L <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K), ]$Z_lm_K <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r), ]$Z_lm_r <- 0.001
  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC), ]$Z_lm_AUC <- 0.001
  
  InteractionScores_AdjustMissing$L_Rank_lm <- NA
  InteractionScores_AdjustMissing$K_Rank_lm <- NA
  InteractionScores_AdjustMissing$r_Rank_lm <- NA
  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
  
  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
  
  # Rank plots
  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1, ])[1])) +
    theme_publication()
  
  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2, ])[1])) +
    theme_publication()
  
  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3, ])[1])) +
    theme_publication()
  
  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1, ])[1])) +
    theme_publication()
  
  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2, ])[1])) +
    theme_publication()
  
  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3, ])[1])) +
    theme_publication()
  
  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  pdf(file.path(outDir, "RankPlots.pdf"), width = 18, height = 12, onefile = TRUE)
  
  grid.arrange(
    Rank_L_1SD,
    Rank_L_2SD,
    Rank_L_3SD,
    Rank_K_1SD,
    Rank_K_2SD,
    Rank_K_3SD,
    ncol = 3,
    nrow = 2
  )

  grid.arrange(
    Rank_L_1SD_notext,
    Rank_L_2SD_notext,
    Rank_L_3SD_notext,
    Rank_K_1SD_notext,
    Rank_K_2SD_notext,
    Rank_K_3SD_notext,
    ncol = 3,
    nrow = 2
  )
  
  dev.off()
  
  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1, ])[1])) +
    theme_publication()
  
  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2, ])[1])) +
    theme_publication()
  
  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3, ])[1])) +
    theme_publication()
  
  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1, ])[1])) +
    theme_publication()
  
  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2, ])[1])) +
    theme_publication()
  
  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3, ])[1])) +
    annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =",
      dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3, ])[1])) +
    theme_publication()
  
  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  pdf(file.path(outDir, "RankPlots_lm.pdf"), width = 18, height = 12, onefile = TRUE)
  
  grid.arrange(
    Rank_L_1SD_lm,
    Rank_L_2SD_lm,
    Rank_L_3SD_lm,
    Rank_K_1SD_lm,
    Rank_K_2SD_lm,
    Rank_K_3SD_lm,
    ncol = 3, nrow = 2
  )
  
  grid.arrange(
    Rank_L_1SD_notext_lm,
    Rank_L_2SD_notext_lm,
    Rank_L_3SD_notext_lm,
    Rank_K_1SD_notext_lm,
    Rank_K_2SD_notext_lm,
    Rank_K_3SD_notext_lm,
    ncol = 3, nrow = 2
  )
  
  dev.off()
  
  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L), ]
  
  # Find overlaps
  X_NArm$Overlap <- "No Effect"
  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2, ]$Overlap <- "Deletion Enhancer Both")
  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2, ]$Overlap <- "Deletion Suppressor Both")
  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2, ]$Overlap <- "Deletion Enhancer lm only")
  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2, ]$Overlap <- "Deletion Enhancer Avg Zscore only")
  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2, ]$Overlap <- "Deletion Suppressor lm only")
  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2, ]$Overlap <- "Deletion Suppressor Avg Zscore only")
  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2, ]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2, ]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
  
  # Get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
  get_lm_L <- lm(X_NArm$Z_lm_L ~ X_NArm$Avg_Zscore_L)
  L_lm <- summary(get_lm_L)
  
  get_lm_K <- lm(X_NArm$Z_lm_K ~ X_NArm$Avg_Zscore_K)
  K_lm <- summary(get_lm_K)
  
  get_lm_r <- lm(X_NArm$Z_lm_r ~ X_NArm$Avg_Zscore_r)
  r_lm <- summary(get_lm_r)
  
  get_lm_AUC <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Avg_Zscore_AUC)
  AUC_lm <- summary(get_lm_AUC)
  
  pdf(file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
  
  print(ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Avg Zscore vs lm L") +
    geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) +
    annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm$r.squared, 2))) + theme_publication_legend_right())
  
  print(ggplot(X_NArm, aes(Avg_Zscore_K, Z_lm_K)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Avg Zscore vs lm K") +
    geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) +
    annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(K_lm$r.squared, 2))) + theme_publication_legend_right())
  
  print(ggplot(X_NArm, aes(Avg_Zscore_r, Z_lm_r)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Avg Zscore vs lm r") +
    geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) +
    annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(r_lm$r.squared, 2))) + theme_publication_legend_right())
  
  print(ggplot(X_NArm, aes(Avg_Zscore_AUC, Z_lm_AUC)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Avg Zscore vs lm AUC") +
    geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) +
    annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(AUC_lm$r.squared, 2))) + theme_publication_legend_right())
  
  dev.off()
  
  lm_v_Zscore_L <- ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L, ORF = OrfRep, Gene = Gene, NG = NG, SM = SM, DB = DB)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +  ggtitle("Avg Zscore vs lm L") +
    geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) +
    annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm$r.squared, 2))) + theme_publication_legend_right()
  
  pgg <- ggplotly(lm_v_Zscore_L)
  plotly_path <- file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.html")
  saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
  
  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
  
  # Get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
  get_lm_L2 <- lm(X_NArm$L_Rank_lm ~ X_NArm$L_Rank)
  L_lm2 <- summary(get_lm_L2)
  get_lm_K2 <- lm(X_NArm$K_Rank_lm ~ X_NArm$K_Rank)
  K_lm2 <- summary(get_lm_K2)
  get_lm_r2 <- lm(X_NArm$r_Rank_lm ~ X_NArm$r_Rank)
  r_lm2 <- summary(get_lm_r2)
  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm ~ X_NArm$AUC_Rank)
  AUC_lm2 <- summary(get_lm_AUC2)
  num_genes_NArm2 <- (dim(X_NArm)[1]) / 2
  
  pdf(file.path(outDir, "Avg_Zscore_vs_lm_ranked_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
  
  print(
    ggplot(X_NArm, aes(L_Rank, L_Rank_lm)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Rank Avg Zscore vs lm L") +
    annotate("text", x = num_genes_NArm2, y = num_genes_NArm2, label = paste("R-squared = ", round(L_lm2$r.squared, 2))) +
    theme_publication_legend_right()
  )
  
  print(
    ggplot(X_NArm, aes(K_Rank, K_Rank_lm)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Rank Avg Zscore vs lm K") +
    annotate("text", x = num_genes_NArm2, y = num_genes_NArm2, label = paste("R-squared = ", round(K_lm2$r.squared, 2))) +
    theme_publication_legend_right()
  )
  
  print(
    ggplot(X_NArm, aes(r_Rank, r_Rank_lm)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Rank Avg Zscore vs lm r") +
    annotate("text", x = num_genes_NArm2, y = num_genes_NArm2, label = paste("R-squared = ", round(r_lm2$r.squared, 2))) +
    theme_publication_legend_right()
  )
  
  print(
    ggplot(X_NArm, aes(AUC_Rank, AUC_Rank_lm)) +
    geom_point(aes(color = Overlap), shape = 3) +
    geom_smooth(method = "lm", color = 1) +
    ggtitle("Rank of Avg Zscore vs lm AUC") +
    annotate("text", x = num_genes_NArm2, y = num_genes_NArm2, label = paste("R-squared = ", round(AUC_lm2$r.squared, 2))) +
    theme_publication_legend_right()
  )
  
  dev.off()
  
  Rank_L_1SD <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_L >= 1, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_L <= -1, ])[1])) +
    theme_publication()
  
  Rank_L_2SD <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_L >= 2, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_L <= -2, ])[1])) +
    theme_publication()
  
  Rank_L_3SD <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_L >= 3, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_L <= -3, ])[1])) +
    theme_publication()
  
  Rank_K_1SD <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_K <= -1, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_K >= 1, ])[1])) +
    theme_publication()
  
  Rank_K_2SD <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_K <= -2, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_K >= 2, ])[1])) +
    theme_publication()
  
  Rank_K_3SD <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Avg_Zscore_K <= -3, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Avg_Zscore_K >= 3, ])[1])) +
    theme_publication()
  
  Rank_L_1SD_notext <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_2SD_notext <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_3SD_notext <- ggplot(X_NArm, aes(L_Rank, Avg_Zscore_L)) +
    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_1SD_notext <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_2SD_notext <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_3SD_notext <- ggplot(X_NArm, aes(K_Rank, Avg_Zscore_K)) +
    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  pdf(file.path(outDir, "RankPlots_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
  
  grid.arrange(
    Rank_L_1SD,
    Rank_L_2SD,
    Rank_L_3SD,
    Rank_K_1SD,
    Rank_K_2SD,
    Rank_K_3SD,
    ncol = 3, nrow = 2
  )

  grid.arrange(
    Rank_L_1SD_notext,
    Rank_L_2SD_notext,
    Rank_L_3SD_notext,
    Rank_K_1SD_notext,
    Rank_K_2SD_notext,
    Rank_K_3SD_notext,
    ncol = 3, nrow = 2
  )
  
  dev.off()
  
  Rank_L_1SD_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_L >= 1, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_L <= -1, ])[1])) +
    theme_publication()
  
  Rank_L_2SD_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_L >= 2, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_L <= -2, ])[1])) +
    theme_publication()
  
  Rank_L_3SD_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_L >= 3, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_L <= -3, ])[1])) +
    theme_publication()
  
  Rank_K_1SD_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_K <= -1, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_K >= 1, ])[1])) +
    theme_publication()
  
  Rank_K_2SD_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_K <= -2, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_K >= 2, ])[1])) +
    theme_publication()
  
  Rank_K_3SD_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = -10, label = paste("Deletion Enhancers =", dim(X_NArm[X_NArm$Z_lm_K <= -3, ])[1])) +
    annotate("text", x = (dim(X_NArm)[1] / 2), y = 10, label = paste("Deletion Suppressors =", dim(X_NArm[X_NArm$Z_lm_K >= 3, ])[1])) +
    theme_publication()
  
  Rank_L_1SD_notext_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_2SD_notext_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_L_3SD_notext_lm <- ggplot(X_NArm, aes(L_Rank_lm, Z_lm_L)) +
    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_1SD_notext_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_2SD_notext_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  Rank_K_3SD_notext_lm <- ggplot(X_NArm, aes(K_Rank_lm, Z_lm_K)) +
    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) +
    annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) +
    geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) +
    theme_publication()
  
  pdf(file.path(outDir, "RankPlots_lm_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
  
  grid.arrange(
    Rank_L_1SD_lm,
    Rank_L_2SD_lm,
    Rank_L_3SD_lm,
    Rank_K_1SD_lm,
    Rank_K_2SD_lm,
    Rank_K_3SD_lm,
    ncol = 3, nrow = 2
  )
  
  grid.arrange(
    Rank_L_1SD_notext_lm,
    Rank_L_2SD_notext_lm,
    Rank_L_3SD_notext_lm,
    Rank_K_1SD_notext_lm,
    Rank_K_2SD_notext_lm,
    Rank_K_3SD_notext_lm,
    ncol = 3, nrow = 2
  )
  
  dev.off()
}

# Get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
get_lm_1 <- lm(X_NArm$Z_lm_K ~ X_NArm$Z_lm_L)
L_lm_1 <- summary(get_lm_1)
get_lm_2 <- lm(X_NArm$Z_lm_r ~ X_NArm$Z_lm_L)
L_lm_2 <- summary(get_lm_2)
get_lm_3 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_L)
L_lm_3 <- summary(get_lm_3)
get_lm_4 <- lm(X_NArm$Z_lm_r ~ X_NArm$Z_lm_K)
L_lm_4 <- summary(get_lm_4)
get_lm_5 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_K)
L_lm_5 <- summary(get_lm_5)
get_lm_6 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_r)
L_lm_6 <- summary(get_lm_6)

pdf(file.path(outDir, "Correlation_CPPs.pdf"), width = 10, height = 7, onefile = TRUE)

ggplot(X_NArm, aes(Z_lm_L, Z_lm_K)) +
  geom_point(shape = 3, color = "gray70") +
  geom_smooth(method = "lm", color = "tomato3") +
  ggtitle("Interaction L vs. Interaction K") +
  xlab("z-score L") +
  ylab("z-score K") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_1$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_L, Z_lm_r)) +
  geom_point(shape = 3, color = "gray70") +
  geom_smooth(method = "lm", color = "tomato3") +
  ggtitle("Interaction L vs. Interaction r") +
  xlab("z-score L") +
  ylab("z-score r") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_2$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_L, Z_lm_AUC)) +
geom_point(shape = 3, color = "gray70") +
geom_smooth(method = "lm", color = "tomato3") +
ggtitle("Interaction L vs. Interaction AUC") +
xlab("z-score L") +
ylab("z-score AUC") +
annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_3$r.squared, 3))) +
theme_publication_legend_right() +
theme(
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  axis.text.x = element_text(size = 16),
  axis.title.x = element_text(size = 18),
  axis.text.y = element_text(size = 16),
  axis.title.y = element_text(size = 18)
)

ggplot(X_NArm, aes(Z_lm_K, Z_lm_r)) +
  geom_point(shape = 3, color = "gray70") +
  geom_smooth(method = "lm", color = "tomato3") +
  ggtitle("Interaction K vs. Interaction r") +
  xlab("z-score K") +
  ylab("z-score r") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_4$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_K, Z_lm_AUC)) +
  geom_point(shape = 3, color = "gray70") +
  geom_smooth(method = "lm", color = "tomato3") +
  ggtitle("Interaction K vs. Interaction AUC") +
  xlab("z-score K") +
  ylab("z-score AUC") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_5$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_r, Z_lm_AUC)) +
  geom_point(shape = 3, color = "gray70") +
  geom_smooth(method = "lm", color = "tomato3") +
  ggtitle("Interaction r vs. Interaction AUC") +
  xlab("z-score r") +
  ylab("z-score AUC") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_6$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18))

InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L), ]

ggplot(X_NArm, aes(Z_lm_L, Z_lm_K)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_K), color = "cyan") +
  ggtitle("Interaction L vs. Interaction K") +
  xlab("z-score L") +
  ylab("z-score K") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_1$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_L, Z_lm_r)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_r), color = "cyan") +
  ggtitle("Interaction L vs. Interaction r") +
  xlab("z-score L") +
  ylab("z-score r") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_2$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_L, Z_lm_AUC)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_AUC), color = "cyan") +
  ggtitle("Interaction L vs. Interaction AUC") +
  xlab("z-score L") +
  ylab("z-score AUC") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_3$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_K, Z_lm_r)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_K, Z_lm_r), color = "cyan") +
  ggtitle("Interaction K vs. Interaction r") +
  xlab("z-score K") +
  ylab("z-score r") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_4$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_K, Z_lm_AUC)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_K, Z_lm_AUC), color = "cyan") +
  ggtitle("Interaction K vs. Interaction AUC") +
  xlab("z-score K") +
  ylab("z-score AUC") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_5$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

ggplot(X_NArm, aes(Z_lm_r, Z_lm_AUC)) +
  geom_point(shape = 3, color = "gray70") +
  geom_point(data = InteractionScores_RF2, aes(Z_lm_r, Z_lm_AUC), color = "cyan") +
  ggtitle("Interaction r vs. Interaction AUC") +
  xlab("z-score r") +
  ylab("z-score AUC") +
  annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_6$r.squared, 3))) +
  theme_publication_legend_right() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(size = 16),
    axis.title.x = element_text(size = 18),
    axis.text.y = element_text(size = 16),
    axis.title.y = element_text(size = 18)
  )

dev.off()

# write.csv(Labels, file.path("../Code/Parameters.csv"), row.names = FALSE)
timestamp()