Figure_S15
Last updated: 2025-08-12
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Knit directory: Paul_CX_2025/
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📌 Corr-motif Boxplots
📌 Non response (0.1 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the non-response group
target_entrez <- 92342 # Ensure this is from prob_1_0.1
# Stop if the gene is not in the non-response group
if (!(target_entrez %in% prob_1_0.1)) {
stop("Selected gene is not in the non-response group for 0.1 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTIREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.1 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.1")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) + # Only facets by Time now
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("Non response (0.1 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 CX_DOX_1
## 📌 CX-DOX Mid-Late Response (0.1 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group Entrez IDs
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
# Load expression matrix
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE) %>% as.data.frame()
# Define gene by Entrez ID
target_entrez <- "9319" # Replace with desired gene from prob_2_0.1
# Check gene membership in the response group
if (!(target_entrez %in% prob_2_0.1)) {
stop("Selected gene is not in the CX-DOX mid-late response group for 0.1 micromolar.")
}
# Extract gene expression
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if (nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- reshape2::melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Annotate metadata
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
) %>%
filter(Concentration == "0.1") # Filter to 0.1 µM only
# Convert factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long$Indv <- ifelse(gene_data_long$Indv %in% names(indv_mapping),
indv_mapping[gene_data_long$Indv], "Unknown")
# Color palette
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Plot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("CX_DOX_1") +
labs(
x = "Drugs",
y = paste0(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 DOX_sp_1
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group files
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX-only mid-late response group
target_entrez <- 1674 # Replace with your desired Entrez ID from prob_3_0.1
# Stop if the gene is not in the DOX-only mid-late response group
if (!(target_entrez %in% prob_3_0.1)) {
stop("Selected gene is not in the DOX-only mid-late response group for 0.1 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- reshape2::melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.1 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.1")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX_sp_1") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 Non response (0.5 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: Non response (0.5 µM)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the non-response group (0.5 µM)
target_entrez <- 9446 # Replace with your desired Entrez ID from prob_1_0.5
# Check if it's in the group
if (!(target_entrez %in% prob_1_0.5)) {
stop("Selected gene is not in the Non response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("Non response (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
## 📌 DOX_sp_2
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX-specific response (0.5 µM)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX-specific response group (0.5 µM)
target_entrez <- 114821 # Replace with your desired Entrez ID from prob_2_0.5
# Check membership
if (!(target_entrez %in% prob_2_0.5)) {
stop("Selected gene is not in the DOX-specific response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX_sp_2") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 DOX_sp_3
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX only mid-late response (0.5 µM)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX only mid-late response group (0.5 µM)
target_entrez <- 4892 # Replace with your desired Entrez ID from prob_3_0.5
# Check membership
if (!(target_entrez %in% prob_3_0.5)) {
stop("Selected gene is not in the DOX only mid-late response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX_sp_2") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 CX_DOX_2
# ----------------- Load Libraries -----------------
library(dplyr)
library(ggplot2)
library(reshape2)
# ----------------- Load Response Group -----------------
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
# ----------------- Load Expression Dataset -----------------
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# ----------------- Choose a Target Gene -----------------
target_entrez <- 100996485 # Replace this with any valid Entrez ID from prob_4_0.5
# Check if gene is valid
if (!(target_entrez %in% prob_4_0.5)) {
stop("Selected gene is not in the CX total + DOX early response group for 0.5 micromolar.")
}
# ----------------- Filter Data for Selected Gene -----------------
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if (nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# ----------------- Reshape to Long Format -----------------
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# ----------------- Extract Metadata -----------------
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# ----------------- Filter for 0.5 µM Only -----------------
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# ----------------- Convert to Factors -----------------
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# ----------------- Map Individual IDs -----------------
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# ----------------- Define Drug Color Palette -----------------
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# ----------------- Extract Gene Symbol -----------------
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# ----------------- Plot -----------------
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("CX_DOX_2") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 CX_DOX_3
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX early + CX-DOX mid-late response (0.5 µM)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX early + CX-DOX mid-late response group (0.5 µM)
target_entrez <- 672 # Replace with your desired Entrez ID from prob_5_0.5
# Check membership
if (!(target_entrez %in% prob_5_0.5)) {
stop("Selected gene is not in the DOX early + CX-DOX mid-late response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("CX_DOX_3") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 ggplot2_3.5.2 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.3.0 promises_1.3.2
[5] tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1 git2r_0.36.2
[9] later_1.3.2 jquerylib_0.1.4 scales_1.3.0 yaml_2.3.10
[13] fastmap_1.2.0 plyr_1.8.9 R6_2.6.1 labeling_0.4.3
[17] generics_0.1.3 workflowr_1.7.1 knitr_1.50 tibble_3.2.1
[21] munsell_0.5.1 rprojroot_2.0.4 bslib_0.9.0 pillar_1.10.2
[25] rlang_1.1.3 cachem_1.1.0 stringi_1.8.3 httpuv_1.6.15
[29] xfun_0.52 fs_1.6.3 sass_0.4.10 cli_3.6.1
[33] withr_3.0.2 magrittr_2.0.3 digest_0.6.34 grid_4.3.0
[37] rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.3
[41] glue_1.7.0 farver_2.1.2 colorspace_2.1-0 rmarkdown_2.29
[45] tools_4.3.0 pkgconfig_2.0.3 htmltools_0.5.8.1