Correlation between CX and DOX logFC
Last updated: 2025-03-02
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📌 Correlation between CX and DOX logFC
📌 Load Required Libraries
# Load necessary libraries
library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.3📌 Correlation Scatter plot
# 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])
# Ensure Entrez_ID is a character across all datasets
datasets <- list(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)
for (i in seq_along(datasets)) {
datasets[[i]]$Entrez_ID <- as.character(datasets[[i]]$Entrez_ID)
}
# Define dataset pairs for correlation analysis
dataset_pairs <- list(
list("CX_0.1_3", CX_0.1_3, "DOX_0.1_3", DOX_0.1_3, "3 hours", "0.1 micromolar"),
list("CX_0.1_24", CX_0.1_24, "DOX_0.1_24", DOX_0.1_24, "24 hours", "0.1 micromolar"),
list("CX_0.1_48", CX_0.1_48, "DOX_0.1_48", DOX_0.1_48, "48 hours", "0.1 micromolar"),
list("CX_0.5_3", CX_0.5_3, "DOX_0.5_3", DOX_0.5_3, "3 hours", "0.5 micromolar"),
list("CX_0.5_24", CX_0.5_24, "DOX_0.5_24", DOX_0.5_24, "24 hours", "0.5 micromolar"),
list("CX_0.5_48", CX_0.5_48, "DOX_0.5_48", DOX_0.5_48, "48 hours", "0.5 micromolar")
)
# Create an empty list to store merged data
merged_data_list <- list()
# Loop through dataset pairs and merge based on Entrez_ID
for (pair in dataset_pairs) {
cx_name <- pair[[1]]
cx_data <- pair[[2]]
dox_name <- pair[[3]]
dox_data <- pair[[4]]
timepoint <- pair[[5]]
concentration <- pair[[6]]
merged_data <- merge(cx_data, dox_data, by = "Entrez_ID", suffixes = c("_CX", "_DOX"))
merged_data$Timepoint <- timepoint
merged_data$Concentration <- concentration
merged_data_list[[paste(cx_name, dox_name, sep = "_vs_")]] <- merged_data
}
# Combine all merged datasets into a single dataframe
combined_data <- do.call(rbind, merged_data_list)
# Select necessary columns and rename them
combined_data <- combined_data %>%
dplyr::select(Entrez_ID, logFC_CX = logFC_CX, logFC_DOX = logFC_DOX, Timepoint, Concentration)
# Ensure timepoints and concentrations are in the correct order
combined_data$Timepoint <- factor(combined_data$Timepoint, levels = c("3 hours", "24 hours", "48 hours"))
combined_data$Concentration <- factor(combined_data$Concentration, levels = c("0.1 micromolar", "0.5 micromolar"))
# **Step 1: Compute global min and max for y-axis scale**
y_min <- min(combined_data$logFC_DOX, na.rm = TRUE)
y_max <- max(combined_data$logFC_DOX, na.rm = TRUE)
# **Step 2: Compute correlations for each dataset with exact p-values**
correlations <- combined_data %>%
group_by(Concentration, Timepoint) %>%
summarise(
r_value = cor(logFC_CX, logFC_DOX, method = "pearson"),
p_value = cor.test(logFC_CX, logFC_DOX, method = "pearson")$p.value,
.groups = "drop"
)
# **Step 3: Display only r-value and whether p < 0.05 or p > 0.05**
correlations <- correlations %>%
mutate(
significance = ifelse(p_value < 0.05, "p < 0.05", "p > 0.05"), # Mark significant comparisons
label = paste0("r = ", round(r_value, 3), "\n", significance)
)
# **Step 4: Create scatter plots faceted by timepoints and concentration**
scatter_plot <- ggplot(combined_data, aes(x = logFC_CX, y = logFC_DOX)) +
geom_point(alpha = 0.6, color = "black") + # Black scatter points
geom_smooth(method = "lm", color = "black", se = FALSE) + # Black regression line
scale_y_continuous(limits = c(y_min, y_max)) + # Fixed Y-axis across all facets
labs(
title = "Correlation between CX and DOX logFC",
x = "logFC (CX)",
y = "logFC (DOX)"
) +
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") + # Ensure same y-axis scale for all facets
geom_text(data = correlations,
aes(x = 1.5, y = y_max * 0.9, label = label),
inherit.aes = FALSE, size = 3, fontface = "bold")
# **Step 5: Display the plot**
print(scatter_plot)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.5.1 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] Matrix_1.6-1.1 gtable_0.3.6 jsonlite_1.8.9 compiler_4.3.0
[5] promises_1.3.0 tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1
[9] git2r_0.35.0 later_1.3.2 jquerylib_0.1.4 splines_4.3.0
[13] scales_1.3.0 yaml_2.3.10 fastmap_1.1.1 lattice_0.22-5
[17] R6_2.5.1 labeling_0.4.3 generics_0.1.3 workflowr_1.7.1
[21] knitr_1.49 tibble_3.2.1 munsell_0.5.1 rprojroot_2.0.4
[25] bslib_0.8.0 pillar_1.10.1 rlang_1.1.3 cachem_1.0.8
[29] stringi_1.8.3 httpuv_1.6.15 xfun_0.50 fs_1.6.3
[33] sass_0.4.9 cli_3.6.1 mgcv_1.9-1 withr_3.0.2
[37] magrittr_2.0.3 digest_0.6.34 grid_4.3.0 rstudioapi_0.17.1
[41] nlme_3.1-166 lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.3
[45] glue_1.7.0 farver_2.1.2 colorspace_2.1-0 rmarkdown_2.29
[49] tools_4.3.0 pkgconfig_2.0.3 htmltools_0.5.8.1