Myeloma dataset comparison
Last updated: 2025-03-01
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Knit directory: CX5461_Project/
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📌 Proportion of Myeloma DE genes in Corrmotif clusters
📌 Load Required Libraries
library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.3library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(tidyr)Warning: package 'tidyr' was built under R version 4.3.3library(org.Hs.eg.db)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'BiocGenerics' was built under R version 4.3.1Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1library(clusterProfiler)Warning: package 'clusterProfiler' was built under R version 4.3.3library(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(gprofiler2)Warning: package 'gprofiler2' was built under R version 4.3.3library(AnnotationDbi)📌 Load Data
# Read the file
file_path <- "data/Myeloma/Myeloma.csv"
Myeloma <- read.csv(file_path, header = TRUE)
# Extract mouse gene symbols
mouse_genes <- Myeloma$Symbol
# Map mouse gene symbols to human homologs
homologs <- gorth(query = mouse_genes,
source_organism = "mmusculus",
target_organism = "hsapiens")
Myeloma_new<- data.frame(homologs$ortholog_name)
# Map gene symbols to Entrez IDs using org.Hs.eg.db
Myeloma_new <- Myeloma_new %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = homologs.ortholog_name,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))📌 Proportion of DE genes across response groups
# Load the saved datasets
prob_all_1 <- read.csv("data/prob_all_1.csv")$Entrez_ID
prob_all_2 <- read.csv("data/prob_all_2.csv")$Entrez_ID
prob_all_3 <- read.csv("data/prob_all_3.csv")$Entrez_ID
prob_all_4 <- read.csv("data/prob_all_4.csv")$Entrez_ID
# Example Response Groups Data (Replace with actual data)
response_groups <- list(
"Non Response" = prob_all_1, # Replace 'prob_all_1', 'prob_all_2', etc. with your actual response group dataframes
"CX_DOX Shared Late Response" = prob_all_2,
"DOX-Specific Response" = prob_all_3,
"Late High Dose DOX-Specific Response" = prob_all_4
)
# Combine response groups into a single dataframe
response_groups_df <- bind_rows(
lapply(response_groups, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Set"
)
# Step 2: Match Overlap Genes with Response Groups
# Classify genes as DEG (match) or Non-DEG (no match)
response_groups_df <- response_groups_df %>%
mutate(
DEG_Status = ifelse(Entrez_ID %in% Myeloma_new$Entrez_ID, "DEG", "Non-DEG")
)
# Step 3: Calculate Proportions
proportion_data <- response_groups_df %>%
group_by(Set, DEG_Status) %>%
summarize(Count = n(), .groups = "drop") %>%
group_by(Set) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# Step 4: Perform Chi-Square Tests (Refactored Version)
# Get counts for the Non Response group
non_response_counts <- proportion_data %>%
filter(Set == "Non Response") %>%
dplyr::select(DEG_Status, Count) %>%
{setNames(.$Count, .$DEG_Status)} # Create named vector for Non Response counts
# Perform chi-square test for selected response groups
chi_results <- proportion_data %>%
filter(Set != "Non Response") %>% # Exclude "Non Response"
group_by(Set) %>%
summarize(
p_value = {
# Extract counts for the current response group
group_counts <- Count[DEG_Status %in% c("DEG", "Non-DEG")]
# Ensure there are no missing categories, fill with 0 if missing
if (!"DEG" %in% DEG_Status) group_counts <- c(group_counts, 0)
if (!"Non-DEG" %in% DEG_Status) group_counts <- c(0, group_counts)
# Create contingency table
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
non_response_counts["DEG"], non_response_counts["Non-DEG"]
), nrow = 2, byrow = TRUE)
# Debugging: Print the contingency table
print(paste("Set:", unique(Set)))
print("Contingency Table:")
print(contingency_table)
# Perform chi-square test
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# Step 5: Merge Results and Plot Proportions
# Merge chi-square results back into proportion data
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Set, Significance), by = "Set")
# Define the correct order for response groups
response_order <- c(
"Non Response",
"CX_DOX Shared Late Response",
"DOX-Specific Response",
"Late High Dose DOX-Specific Response"
)
proportion_data$Set <- factor(proportion_data$Set, levels = response_order)
# Plot proportions with significance stars
ggplot(proportion_data, aes(x = Set, y = Percentage, fill = DEG_Status)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = proportion_data %>% distinct(Set, Significance),
aes(x = Set, y = 105, label = Significance), # Position stars above bars
inherit.aes = FALSE,
size = 6,
color = "black",
hjust = 0.5
) +
scale_fill_manual(values = c("DEG" = "#e41a1c", "Non-DEG" = "#377eb8")) +
labs(
title = "Proportion of DEGs and Non-DEGs Across Response Groups (Myeloma Study)",
x = "Response Groups",
y = "Percentage",
fill = "Category"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
📌 Proportion of Myeloma genes in CX and DOX DEGs
📌 Read and Process Data
# Load DEGs Data
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Extract Significant DEGs
DEG1 <- as.character(CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05])
DEG2 <- as.character(CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05])
DEG3 <- as.character(CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05])
DEG4 <- as.character(CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05])
DEG5 <- as.character(CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05])
DEG6 <- as.character(CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05])
DEG7 <- as.character(DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05])
DEG8 <- as.character(DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05])
DEG9 <- as.character(DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05])
DEG10 <- as.character(DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05])
DEG11 <- as.character(DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05])
DEG12 <- as.character(DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05])📌 Proportion of Myeloma genes in CX and DOX DEGs datasets
# Define CX-5461 DEG lists
CX_DEGs <- list(
"CX_0.1_3" = DEG1, "CX_0.1_24" = DEG2, "CX_0.1_48" = DEG3,
"CX_0.5_3" = DEG4, "CX_0.5_24" = DEG5, "CX_0.5_48" = DEG6
)
# Define DOX DEG lists
DOX_DEGs <- list(
"DOX_0.1_3" = DEG7, "DOX_0.1_24" = DEG8, "DOX_0.1_48" = DEG9,
"DOX_0.5_3" = DEG10, "DOX_0.5_24" = DEG11, "DOX_0.5_48" = DEG12
)
# Load Myeloma_new dataset (Use `Entrez_ID` for matching)
Myeloma_genes <- na.omit(Myeloma_new$Entrez_ID) # Keep only Entrez_IDs
# **Process CX-5461 Samples**
CX_DEGs_df <- bind_rows(
lapply(CX_DEGs, function(ids) data.frame(Entrez_ID = ids, Sample_Type = "CX-5461")),
.id = "Sample"
) %>%
mutate(Category = ifelse(Entrez_ID %in% Myeloma_genes, "Yes", "No")) %>%
group_by(Sample, Sample_Type, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Sample, Sample_Type) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **Process DOX Samples**
DOX_DEGs_df <- bind_rows(
lapply(DOX_DEGs, function(ids) data.frame(Entrez_ID = ids, Sample_Type = "DOX")),
.id = "Sample"
) %>%
mutate(Category = ifelse(Entrez_ID %in% Myeloma_genes, "Yes", "No")) %>%
group_by(Sample, Sample_Type, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Sample, Sample_Type) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **Merge CX and DOX Dataframes**
proportion_data <- bind_rows(CX_DEGs_df, DOX_DEGs_df)
# **Ensure "Yes" is at the Bottom and "No" is at the Top**
proportion_data$Category <- factor(proportion_data$Category, levels = c("Yes", "No"))
# Define correct order for samples
sample_order <- c(
"CX_0.1_3", "CX_0.1_24", "CX_0.1_48", "CX_0.5_3", "CX_0.5_24", "CX_0.5_48",
"DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48", "DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48"
)
proportion_data$Sample <- factor(proportion_data$Sample, levels = sample_order)
# Save proportion data
write.csv(proportion_data, "C:/Work/Postdoc_UTMB/CX-5461 Project/Transcriptome literatures/lit2/Proportion_CX_DOX_Myeloma_fixed.csv", row.names = FALSE)
# **Generate Proportion Plot for CX-5461 and DOX Separately**
ggplot(proportion_data, aes(x = Sample, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
facet_wrap(~Sample_Type, scales = "free_x") + # Separate CX-5461 and DOX
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 100)) + # Y-axis as percentage
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red) at Bottom, No (Blue) on Top
labs(
title = "Proportion of Myeloma Genes in CX-5461 and DOX DEGs",
x = "Samples (CX-5461 and DOX)",
y = "Percentage",
fill = "Category"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2), # Updated for ggplot2 3.4.0+
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Correlation of Myeloma genes with CX and DOX expressed genes
📌 Correlation of Myeloma genes with CX expressed genes
# **Step 1: Map Gene Symbols to Entrez IDs using org.Hs.eg.db**
Myeloma <- Myeloma %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
# **Step 2: Convert Entrez_ID to character to avoid merge issues**
Myeloma$Entrez_ID <- as.character(Myeloma$Entrez_ID)
CX_0.1_3$Entrez_ID <- as.character(CX_0.1_3$Entrez_ID)
CX_0.5_3$Entrez_ID <- as.character(CX_0.5_3$Entrez_ID)
CX_0.1_24$Entrez_ID <- as.character(CX_0.1_24$Entrez_ID)
CX_0.5_24$Entrez_ID <- as.character(CX_0.5_24$Entrez_ID)
CX_0.1_48$Entrez_ID <- as.character(CX_0.1_48$Entrez_ID)
CX_0.5_48$Entrez_ID <- as.character(CX_0.5_48$Entrez_ID)
# **Step 3: Merge Myeloma dataset with CX at different concentrations & timepoints**
merged_CX_0.1_3 <- merge(Myeloma, CX_0.1_3, by = "Entrez_ID")
merged_CX_0.5_3 <- merge(Myeloma, CX_0.5_3, by = "Entrez_ID")
merged_CX_0.1_24 <- merge(Myeloma, CX_0.1_24, by = "Entrez_ID")
merged_CX_0.5_24 <- merge(Myeloma, CX_0.5_24, by = "Entrez_ID")
merged_CX_0.1_48 <- merge(Myeloma, CX_0.1_48, by = "Entrez_ID")
merged_CX_0.5_48 <- merge(Myeloma, CX_0.5_48, by = "Entrez_ID")
# **Step 4: Remove NA values**
merged_CX_0.1_3 <- na.omit(merged_CX_0.1_3)
merged_CX_0.5_3 <- na.omit(merged_CX_0.5_3)
merged_CX_0.1_24 <- na.omit(merged_CX_0.1_24)
merged_CX_0.5_24 <- na.omit(merged_CX_0.5_24)
merged_CX_0.1_48 <- na.omit(merged_CX_0.1_48)
merged_CX_0.5_48 <- na.omit(merged_CX_0.5_48)
# **Step 5: Rename columns to avoid conflicts**
colnames(merged_CX_0.1_3) <- colnames(merged_CX_0.5_3) <-
colnames(merged_CX_0.1_24) <- colnames(merged_CX_0.5_24) <-
colnames(merged_CX_0.1_48) <- colnames(merged_CX_0.5_48) <-
c("Entrez_ID", "Symbol_Myeloma", "logFC_Myeloma", "logFC_CX", "AveExpr_CX", "t_CX", "P.Value_CX", "adj.P.Val_CX", "B_CX")
# **Step 6: Add timepoint and concentration labels for faceting**
merged_CX_0.1_3$Timepoint <- "3hr"
merged_CX_0.5_3$Timepoint <- "3hr"
merged_CX_0.1_24$Timepoint <- "24hr"
merged_CX_0.5_24$Timepoint <- "24hr"
merged_CX_0.1_48$Timepoint <- "48hr"
merged_CX_0.5_48$Timepoint <- "48hr"
merged_CX_0.1_3$Concentration <- "0.1 µM"
merged_CX_0.5_3$Concentration <- "0.5 µM"
merged_CX_0.1_24$Concentration <- "0.1 µM"
merged_CX_0.5_24$Concentration <- "0.5 µM"
merged_CX_0.1_48$Concentration <- "0.1 µM"
merged_CX_0.5_48$Concentration <- "0.5 µM"
# **Step 7: Combine all datasets into a single data frame**
merged_data_Myeloma <- rbind(
merged_CX_0.1_3[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_CX_0.5_3[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_CX_0.1_24[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_CX_0.5_24[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_CX_0.1_48[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_CX_0.5_48[, c("Entrez_ID", "logFC_CX", "logFC_Myeloma", "Timepoint", "Concentration")]
)
# **Ensure timepoints are in correct order**
merged_data_Myeloma$Timepoint <- factor(merged_data_Myeloma$Timepoint, levels = c("3hr", "24hr", "48hr"))
# **Step 8: Compute correlations for each facet**
correlations <- merged_data_Myeloma %>%
group_by(Concentration, Timepoint) %>%
summarise(
r_value = cor(logFC_CX, logFC_Myeloma, method = "pearson"),
p_value = cor.test(logFC_CX, logFC_Myeloma, method = "pearson")$p.value,
.groups = "drop"
)
# **Step 9: Create correlation annotation data**
correlation_data <- correlations %>%
mutate(
x = 1.5, # Adjusted to fit within fixed axis range (-5 to 2)
y = max(merged_data_Myeloma$logFC_Myeloma, na.rm = TRUE) * 0.85,
label = paste0("r = ", round(r_value, 3), "\np = ", signif(p_value, 3))
)
# **Step 10: Create styled scatter plot with fixed X-axis range and ordered timepoints**
scatter_plot_Myeloma <- ggplot(merged_data_Myeloma, aes(x = logFC_CX, y = logFC_Myeloma)) +
geom_point(alpha = 0.6, color = "black") +
geom_smooth(method = "lm", color = "black", se = FALSE) +
scale_x_continuous(limits = c(-5, 2)) + # Fixed X-axis range
labs(
title = "Correlation between CX and Myeloma logFC",
x = "logFC (CX)",
y = "logFC (Myeloma)"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 2), # Outer border
strip.background = element_rect(fill = "white", color = "black", linewidth = 1.5),
strip.text = element_text(size = 12, face = "bold", color = "black")
) +
facet_grid(Timepoint ~ Concentration, scales = "fixed") + # Ensures correct timepoint order
geom_text(data = correlation_data,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, size = 3, fontface = "bold")
# **Step 11: Display the plot**
print(scatter_plot_Myeloma)Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
📌 Correlation of Myeloma genes with DOX expressed genes
# **Step 1: Map Gene Symbols to Entrez IDs using org.Hs.eg.db**
Myeloma <- Myeloma %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
# **Step 2: Convert Entrez_ID to character to avoid merge issues**
Myeloma$Entrez_ID <- as.character(Myeloma$Entrez_ID)
DOX_0.1_3$Entrez_ID <- as.character(DOX_0.1_3$Entrez_ID)
DOX_0.5_3$Entrez_ID <- as.character(DOX_0.5_3$Entrez_ID)
DOX_0.1_24$Entrez_ID <- as.character(DOX_0.1_24$Entrez_ID)
DOX_0.5_24$Entrez_ID <- as.character(DOX_0.5_24$Entrez_ID)
DOX_0.1_48$Entrez_ID <- as.character(DOX_0.1_48$Entrez_ID)
DOX_0.5_48$Entrez_ID <- as.character(DOX_0.5_48$Entrez_ID)
# **Step 3: Merge Myeloma dataset with DOX at different concentrations & timepoints**
merged_DOX_0.1_3 <- merge(Myeloma, DOX_0.1_3, by = "Entrez_ID")
merged_DOX_0.5_3 <- merge(Myeloma, DOX_0.5_3, by = "Entrez_ID")
merged_DOX_0.1_24 <- merge(Myeloma, DOX_0.1_24, by = "Entrez_ID")
merged_DOX_0.5_24 <- merge(Myeloma, DOX_0.5_24, by = "Entrez_ID")
merged_DOX_0.1_48 <- merge(Myeloma, DOX_0.1_48, by = "Entrez_ID")
merged_DOX_0.5_48 <- merge(Myeloma, DOX_0.5_48, by = "Entrez_ID")
# **Step 4: Remove NA values**
merged_DOX_0.1_3 <- na.omit(merged_DOX_0.1_3)
merged_DOX_0.5_3 <- na.omit(merged_DOX_0.5_3)
merged_DOX_0.1_24 <- na.omit(merged_DOX_0.1_24)
merged_DOX_0.5_24 <- na.omit(merged_DOX_0.5_24)
merged_DOX_0.1_48 <- na.omit(merged_DOX_0.1_48)
merged_DOX_0.5_48 <- na.omit(merged_DOX_0.5_48)
# **Step 5: Rename columns to avoid conflicts**
colnames(merged_DOX_0.1_3) <- colnames(merged_DOX_0.5_3) <-
colnames(merged_DOX_0.1_24) <- colnames(merged_DOX_0.5_24) <-
colnames(merged_DOX_0.1_48) <- colnames(merged_DOX_0.5_48) <-
c("Entrez_ID", "Symbol_Myeloma", "logFC_Myeloma", "logFC_DOX", "AveExpr_DOX", "t_DOX", "P.Value_DOX", "adj.P.Val_DOX", "B_DOX")
# **Step 6: Add timepoint and concentration labels for faceting**
merged_DOX_0.1_3$Timepoint <- "3hr"
merged_DOX_0.5_3$Timepoint <- "3hr"
merged_DOX_0.1_24$Timepoint <- "24hr"
merged_DOX_0.5_24$Timepoint <- "24hr"
merged_DOX_0.1_48$Timepoint <- "48hr"
merged_DOX_0.5_48$Timepoint <- "48hr"
merged_DOX_0.1_3$Concentration <- "0.1 µM"
merged_DOX_0.5_3$Concentration <- "0.5 µM"
merged_DOX_0.1_24$Concentration <- "0.1 µM"
merged_DOX_0.5_24$Concentration <- "0.5 µM"
merged_DOX_0.1_48$Concentration <- "0.1 µM"
merged_DOX_0.5_48$Concentration <- "0.5 µM"
# **Step 7: Combine all datasets into a single data frame**
merged_data_DOX <- rbind(
merged_DOX_0.1_3[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_DOX_0.5_3[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_DOX_0.1_24[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_DOX_0.5_24[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_DOX_0.1_48[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")],
merged_DOX_0.5_48[, c("Entrez_ID", "logFC_DOX", "logFC_Myeloma", "Timepoint", "Concentration")]
)
# **Ensure timepoints are in correct order**
merged_data_DOX$Timepoint <- factor(merged_data_DOX$Timepoint, levels = c("3hr", "24hr", "48hr"))
# **Step 8: Compute correlations for each facet**
correlations <- merged_data_DOX %>%
group_by(Concentration, Timepoint) %>%
summarise(
r_value = cor(logFC_DOX, logFC_Myeloma, method = "pearson"),
p_value = cor.test(logFC_DOX, logFC_Myeloma, method = "pearson")$p.value,
.groups = "drop"
)
# **Step 9: Create correlation annotation data**
correlation_data <- correlations %>%
mutate(
x = 1.5, # Adjusted to fit within fixed axis range (-5 to 2)
y = max(merged_data_DOX$logFC_Myeloma, na.rm = TRUE) * 0.85,
label = paste0("r = ", round(r_value, 3), "\np = ", signif(p_value, 3))
)
# **Step 10: Create styled scatter plot with fixed X-axis range and ordered timepoints**
scatter_plot_DOX <- ggplot(merged_data_DOX, aes(x = logFC_DOX, y = logFC_Myeloma)) +
geom_point(alpha = 0.6, color = "black") +
geom_smooth(method = "lm", color = "black", se = FALSE) +
scale_x_continuous(limits = c(-5, 2)) + # Fixed X-axis range
labs(
title = "Correlation between DOX and Myeloma logFC",
x = "logFC (DOX)",
y = "logFC (Myeloma)"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 2),
strip.background = element_rect(fill = "white", color = "black", linewidth = 1.5),
strip.text = element_text(size = 12, face = "bold", color = "black")
) +
facet_grid(Timepoint ~ Concentration, scales = "fixed") +
geom_text(data = correlation_data,
aes(x = x, y = y, label = label),
inherit.aes = FALSE, size = 3, fontface = "bold")
# **Step 11: Display the plot**
print(scatter_plot_DOX)Warning: Removed 16 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 16 rows containing missing values or values outside the scale range
(`geom_point()`).
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gprofiler2_0.2.3 biomaRt_2.58.2 clusterProfiler_4.10.1
[4] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 IRanges_2.36.0
[7] S4Vectors_0.40.1 Biobase_2.62.0 BiocGenerics_0.48.1
[10] tidyr_1.3.1 dplyr_1.1.4 ggplot2_3.5.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 farver_2.1.2 rmarkdown_2.29
[7] fs_1.6.3 zlibbioc_1.48.0 vctrs_0.6.5
[10] memoise_2.0.1 RCurl_1.98-1.13 ggtree_3.10.1
[13] htmltools_0.5.8.1 progress_1.2.3 curl_6.0.1
[16] gridGraphics_0.5-1 sass_0.4.9 bslib_0.8.0
[19] htmlwidgets_1.6.4 plyr_1.8.9 plotly_4.10.4
[22] cachem_1.0.8 igraph_2.1.1 lifecycle_1.0.4
[25] pkgconfig_2.0.3 Matrix_1.6-1.1 R6_2.5.1
[28] fastmap_1.1.1 gson_0.1.0 GenomeInfoDbData_1.2.11
[31] digest_0.6.34 aplot_0.2.3 enrichplot_1.22.0
[34] colorspace_2.1-0 patchwork_1.3.0 rprojroot_2.0.4
[37] RSQLite_2.3.3 labeling_0.4.3 filelock_1.0.3
[40] mgcv_1.9-1 httr_1.4.7 polyclip_1.10-7
[43] compiler_4.3.0 bit64_4.0.5 withr_3.0.2
[46] BiocParallel_1.36.0 viridis_0.6.5 DBI_1.2.3
[49] ggforce_0.4.2 MASS_7.3-60 rappdirs_0.3.3
[52] HDO.db_0.99.1 tools_4.3.0 ape_5.8
[55] scatterpie_0.2.4 httpuv_1.6.15 glue_1.7.0
[58] nlme_3.1-166 GOSemSim_2.28.1 promises_1.3.0
[61] grid_4.3.0 shadowtext_0.1.4 reshape2_1.4.4
[64] fgsea_1.28.0 generics_0.1.3 gtable_0.3.6
[67] data.table_1.14.10 hms_1.1.3 xml2_1.3.6
[70] tidygraph_1.3.1 XVector_0.42.0 ggrepel_0.9.6
[73] pillar_1.10.1 stringr_1.5.1 yulab.utils_0.1.8
[76] later_1.3.2 splines_4.3.0 tweenr_2.0.3
[79] BiocFileCache_2.10.2 treeio_1.26.0 lattice_0.22-5
[82] bit_4.0.5 tidyselect_1.2.1 GO.db_3.18.0
[85] Biostrings_2.70.1 knitr_1.49 git2r_0.35.0
[88] gridExtra_2.3 xfun_0.50 graphlayouts_1.2.0
[91] stringi_1.8.3 workflowr_1.7.1 lazyeval_0.2.2
[94] ggfun_0.1.8 yaml_2.3.10 evaluate_1.0.3
[97] codetools_0.2-20 ggraph_2.2.1 tibble_3.2.1
[100] qvalue_2.34.0 ggplotify_0.1.2 cli_3.6.1
[103] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.12
[106] GenomeInfoDb_1.38.8 dbplyr_2.5.0 png_0.1-8
[109] XML_3.99-0.17 parallel_4.3.0 blob_1.2.4
[112] prettyunits_1.2.0 DOSE_3.28.2 bitops_1.0-7
[115] viridisLite_0.4.2 tidytree_0.4.6 scales_1.3.0
[118] purrr_1.0.2 crayon_1.5.3 rlang_1.1.3
[121] cowplot_1.1.3 fastmatch_1.1-4 KEGGREST_1.42.0