Figure 2 Analysis
Last updated: 2026-01-06
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Knit directory: DXR_website/
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| html | c382b38 | emmapfort | 2026-01-06 | Build site. |
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| Rmd | da1f69c | emmapfort | 2025-12-02 | updated analysis 12/02/25 |
Libraries
Load necessary libraries for analysis.
library(tidyverse)
library(readxl)
library(readr)
library(ggrepel)
library(ggfortify)
library(RUVSeq)
library(patchwork)
library(eulerr)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(ggVennDiagram)
library(ggrastr)Define my custom plot theme
# Define the custom theme
# plot_theme_custom <- function() {
# theme_minimal() +
# theme(
# #line for x and y axis
# axis.line = element_line(linewidth = 1,
# color = "black"),
#
# #axis ticks only on x and y, length standard
# axis.ticks.x = element_line(color = "black",
# linewidth = 1),
# axis.ticks.y = element_line(color = "black",
# linewidth = 1),
# axis.ticks.length = unit(0.05, "in"),
#
# #text and font
# axis.text = element_text(color = "black",
# family = "Arial",
# size = 7),
# axis.title = element_text(color = "black",
# family = "Arial",
# size = 9),
# legend.text = element_text(color = "black",
# family = "Arial",
# size = 7),
# legend.title = element_text(color = "black",
# family = "Arial",
# size = 9),
# plot.title = element_text(color = "black",
# family = "Arial",
# size = 10),
#
# #blank background and border
# panel.background = element_blank(),
# panel.border = element_blank(),
#
# #gridlines for alignment
# panel.grid.major = element_line(color = "grey80", linewidth = 0.5), #grey major grid for align in illus
# panel.grid.minor = element_line(color = "grey90", linewidth = 0.5) #grey minor grid for align in illus
# )
# }
# saveRDS(plot_theme_custom, "data/plot_theme_custom.RDS")
theme_custom <- readRDS("data/plot_theme_custom.RDS")####Colors####
# txtime_col_old <- list(
# "DOX_24T" = "#263CA5",
# "VEH_24T" = "#444343",
# "DOX_24R" = "#5B8FD1",
# "VEH_24R" = "#757575",
# "DOX_144R" = "#89C5E5",
# "VEH_144R" = "#AAAAAA"
# )
#updated colors and factors:
ind_col <- list(
"1" = "#FF9F64",
"2" = "#78EBDA",
"3" = "#ADCD77",
"4" = "#E6CC50",
"5" = "#A68AC0",
"6" = "#F16F90",
"6R" = "#C61E4E"
)
ind_col_nr <- list(
"1" = "#FF9F64",
"2" = "#78EBDA",
"3" = "#ADCD77",
"4" = "#E6CC50",
"5" = "#A68AC0",
"6" = "#F16F90"
)
time_col <- list(
"t0" = "#005A4C",
"t24" = "#328477",
"t144" = "#8FB9B1")
tx_col <- list(
"DOX" = "#499FBD",
"VEH" = "#BBBBBC")
txtime_col <- list(
"DOX_t0" = "#263CA5",
"VEH_t0" = "#444343",
"DOX_t24" = "#5B8FD1",
"VEH_t24" = "#757575",
"DOX_t144" = "#89C5E5",
"VEH_t144" = "#AAAAAA"
)Define a function to save plots as .pdf and .png
save_plot <- function(plot, filename,
folder = ".",
width = 8,
height = 6,
units = "in",
dpi = 300,
add_date = TRUE) {
if (missing(filename)) stop("Please provide a filename (without extension) for the plot.")
date_str <- if (add_date) paste0("_", format(Sys.Date(), "%y%m%d")) else ""
pdf_file <- file.path(folder, paste0(filename, date_str, ".pdf"))
png_file <- file.path(folder, paste0(filename, date_str, ".png"))
ggsave(filename = pdf_file, plot = plot, device = cairo_pdf, width = width, height = height, units = units, bg = "transparent")
ggsave(filename = png_file, plot = plot, device = "png", width = width, height = height, units = units, dpi = dpi, bg = "transparent")
message("Saved plot as Cairo PDF: ", pdf_file)
message("Saved plot as PNG: ", png_file)
}
output_folder <- "C:/Users/emmap/OneDrive/Desktop/DXR Manuscript Materials/Fig pdfs"
#save plot function created
#to use: just define the plot name, filename_base, width, heightFigure 2A - PCA following RUVs Correction
Principal component analysis of RNAseq data following RUVs correction.
#read in dataframe after cpm transformation and k=1 RUVs correction
#add in the annotation files
ind_num <- readRDS("data/QC/ind_num.RDS")
annot <- read.csv("data/QC/Metadata_update.csv")
RUV_df_rm1_cpm <- readRDS("data/Fig2/RUV_df_rm1_cpm.RDS")
prcomp_res_1_cpm <- prcomp(t(RUV_df_rm1_cpm), scale. = FALSE, center = TRUE)
annot_prcomp_res_1_cpm <- readRDS("data/Fig2/annot_pca_cpm_fig2a.RDS")
fig2a <- autoplot(prcomp_res_1_cpm,
data = annot,
colour = "Cond",
shape = "Time",
size = 5,
x = 1,
y = 2) +
theme_custom() +
scale_color_manual(values = txtime_col) +
geom_text_repel(aes(label = ind_num),
nudge_y = 0.05,
max.overlaps = 100,
segment.color = "grey40") +
ggtitle("RUVs Correction k=1 log2cpm")
print(fig2a)
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#save for figure generation
# save_plot(plot = fig2a,
# filename = "Fig2A_PCA_6x8_EMP",
# folder = output_folder,
# width = 6,
# height = 8)Differential Expression Analysis - RUVs Corrected
#Perform Differential Expression Analysis with k = 1 RUVs Correction
#same DGEList object as before
dge <- readRDS("data/DE/dge_matrix.RDS")
Metadata_2 <- readRDS("data/QC/Metadata_2_norep_update.RDS")
x_norep <- readRDS("data/DE/x_norep.RDS")
#check normalization factors from TMM normalization of LIBRARIES
dge$samples group lib.size norm.factors
87-1_DOX_24 DOX_t0 19935097 1.0526605
87-1_DMSO_24 VEH_t0 21302879 0.9773889
87-1_DOX_24+24 DOX_t24 25636959 1.0751043
87-1_DMSO_24+24 VEH_t24 26319662 0.9940323
87-1_DOX_24+144 DOX_t144 23463426 0.9003102
87-1_DMSO_24+144 VEH_t144 25840938 0.9888449
78-1_DOX_24 DOX_t0 23085807 0.7676077
78-1_DMSO_24 VEH_t0 25610495 1.0077383
78-1_DOX_24+24 DOX_t24 18083930 1.1682704
78-1_DMSO_24+24 VEH_t24 24331177 0.9906872
78-1_DOX_24+144 DOX_t144 19754391 0.9941834
78-1_DMSO_24+144 VEH_t144 22641509 1.0010734
75-1_DOX_24 DOX_t0 20583626 1.0676786
75-1_DMSO_24 VEH_t0 28166198 1.0031906
75-1_DOX_24+24 DOX_t24 25831427 1.1530208
75-1_DMSO_24+24 VEH_t24 26081158 1.0058953
75-1_DOX_24+144 DOX_t144 24659898 0.9261599
75-1_DMSO_24+144 VEH_t144 25412931 0.9703454
84-1_DOX_24 DOX_t0 23393931 0.9745263
84-1_DMSO_24 VEH_t0 22853195 0.9565797
84-1_DOX_24+24 DOX_t24 23846995 1.1659432
84-1_DMSO_24+24 VEH_t24 21299355 0.9649641
84-1_DOX_24+144 DOX_t144 18222568 0.9913625
84-1_DMSO_24+144 VEH_t144 28115884 0.9653464
17-3_DOX_24 DOX_t0 22518848 0.9766893
17-3_DMSO_24 VEH_t0 24589534 0.9612345
17-3_DOX_24+24 DOX_t24 24797547 1.1703079
17-3_DMSO_24+24 VEH_t24 25977536 0.9509690
17-3_DOX_24+144 DOX_t144 27447106 0.9422729
17-3_DMSO_24+144 VEH_t144 24893583 0.9356377
90-1_DOX_24 DOX_t0 25187428 1.0311957
90-1_DMSO_24 VEH_t0 25630519 1.0283437
90-1_DOX_24+24 DOX_t24 26138399 1.1183471
90-1_DMSO_24+24 VEH_t24 24430396 0.9988688
90-1_DOX_24+144 DOX_t144 23323463 0.9496884
90-1_DMSO_24+144 VEH_t144 25424152 0.9872926#read in my covariate information RUV_1 dataframe + remove 6R
RUV_df_rm1 <- readRDS("data/DE/RUV_df_rm1.RDS")
#filter out 6R for DE analysis
RUV_1_df_filt <- RUV_df_rm1[!grepl("6R$", rownames(RUV_df_rm1)), , drop = FALSE]
Metadata_2$W_1 <- RUV_1_df_filt$W_1
#now ensure that RUV_1 has the right number of cols after removing rep
length(RUV_1_df_filt$W_1)[1] 36#36
#now make this into a list
RUV_1_DE <- RUV_1_df_filt$W_1
# saveRDS(RUV_1_DE, "data/DE/RUV_1_DE.RDS")
#create my design matrix for DE + RUVs covariate k=1
design1 <- model.matrix(~0 + RUV_1_DE + Metadata_2$Condition)
colnames(design1) <- gsub("Metadata_2\\$Condition", "", colnames(design1))
#take care that the matrix automatically sorts cols alphabetically
#run duplicate correlation for individual effect
corfit1 <- duplicateCorrelation(object = dge$counts, design = design1, block = Metadata_2$Ind)
#voom transformation and plot
v1 <- voom(dge, design1, block = Metadata_2$Ind, correlation = corfit1$consensus.correlation, plot = TRUE)
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#fit my linear model
fit1 <- lmFit(v1, design1, block = Metadata_2$Ind, correlation = corfit1$consensus.correlation)
#make my contrast matrix to compare across tx and veh
contrast_matrix_RUV <- makeContrasts(
V.D24T = DOX_t0 - VEH_t0,
V.D24R = DOX_t24 - VEH_t24,
V.D144R = DOX_t144 - VEH_t144,
RUV_1_24T = RUV_1_DE - VEH_t0,
RUV_1_24R = RUV_1_DE - VEH_t24,
RUV_1_144R = RUV_1_DE - VEH_t144,
levels = design1
)
#apply these contrasts to compare DOX to DMSO VEH
fit_RUV <- contrasts.fit(fit1, contrast_matrix_RUV)
fit_RUV <- eBayes(fit_RUV)
#plot the mean-variance trend
plotSA(fit_RUV, main = "Mean-Variance Trend, RUVs k=1")
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#look at the summary of your results
##this tells you the number of DEGs in each condition
results_summary1 <- decideTests(fit_RUV, adjust.method = "BH", p.value = 0.05)
summary(results_summary1) V.D24T V.D24R V.D144R RUV_1_24T RUV_1_24R RUV_1_144R
Down 4866 3727 466 10518 10519 10513
NotSig 4899 7107 13659 2905 2899 2889
Up 4554 3485 194 896 901 917# V.D24T V.D24R V.D144R
# Down 4866 3727 466
# NotSig 4899 7107 13659
# Up 4554 3485 194
#compare this to my previous DEGs I found
# summary(results_summary)
# V.D24T V.D24R V.D144R
# Down 4723 3593 359
# NotSig 5076 7151 13810
# Up 4520 3575 150
#overall, there are more DEGs found after RUVs k=1 correction
#this was an expected result as it increases tx effect w/ correctionToptables
##Generate Toptables RUVs
# Generate Top Table for Specific Comparisons
x <- readRDS("data/DE/x_dge_counts_allind_updated.RDS")
Toptable_V.D24T_k1 <- topTable(fit = fit_RUV, coef = "V.D24T", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
# write.csv(Toptable_V.D24T_k1, "data/DE/Toptable_V.D24T_k1.csv")
Toptable_V.D24R_k1 <- topTable(fit = fit_RUV, coef = "V.D24R", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
# write.csv(Toptable_V.D24R_k1, "data/DE/Toptable_V.D24R_k1.csv")
Toptable_V.D144R_k1 <- topTable(fit = fit_RUV, coef = "V.D144R", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
# write.csv(Toptable_V.D144R_k1, "data/DE/Toptable_V.D144R_k1.csv")
# save all of these toptables as R objects
# saveRDS(list(
# V.D24T_k1 = Toptable_V.D24T_k1,
# V.D24R_k1 = Toptable_V.D24R_k1,
# V.D144R_k1 = Toptable_V.D144R_k1
# ), file = "data/DE/Toptable_list_RUVk1.RDS")
# Toptable_list_RUVk1 <- readRDS("data/DE/Toptable_list_RUVk1.RDS")
#################################################################
#this section is commented out since it only needs to run once
#kept the code for posterity
# #I want to add the hgnc symbols to my toptables as well
# ####D24T####
# #load in data
# sample_toptab_24T <- read_csv("C:/Users/emmap/RDirectory/Recovery_RNAseq/Recovery_5FU/data/new/DEGs/Toptable_V.D24T_k1.csv", show_col_types = FALSE)
# # ----------------- Ensure Entrez_ID is Present and in Character Format -----------------
# # Check column names
# print(colnames(sample_toptab_24T))
# # Rename if needed (adjust if the column name is not exactly 'Entrez_ID')
# colnames(sample_toptab_24T)[1] <- "Entrez_ID"
# # Convert Entrez_ID to character
# sample_toptab_24T <- sample_toptab_24T %>%
# mutate(Entrez_ID = as.character(Entrez_ID))
# # ----------------- Map Entrez_ID to Gene Symbol -----------------
# gene_symbols1 <- AnnotationDbi::select(
# org.Hs.eg.db,
# keys = sample_toptab_24T$Entrez_ID,
# columns = c("SYMBOL"),
# keytype = "ENTREZID"
# )
# # ----------------- Join Back to Main Data -----------------
# Toptable_RUV_24T <- left_join(sample_toptab_24T, gene_symbols1, by = c("Entrez_ID" = "ENTREZID"))
# Toptable_RUV_24T %>% column_to_rownames(., var = "Entrez_ID")
# # ----------------- Save Annotated Output -----------------
# write_csv(Toptable_RUV_24T, "data/DE/Toptable_RUV_24T.csv")
#
# Toptable_RUV_24T <- read.csv("data/DE/Toptable_RUV_24T.csv")
# #now do this again for my other two toptables
#
# ####24R####
# #load in data
# sample_toptab_24R <- read_csv("C:/Users/emmap/RDirectory/Recovery_RNAseq/Recovery_5FU/data/new/DEGs/Toptable_V.D24R_k1.csv", show_col_types = FALSE)
# # ----------------- Ensure Entrez_ID is Present and in Character Format -----------------
# # Check column names
# print(colnames(sample_toptab_24R))
# # Rename if needed (adjust if the column name is not exactly 'Entrez_ID')
# colnames(sample_toptab_24R)[1] <- "Entrez_ID"
# # Convert Entrez_ID to character
# sample_toptab_24R <- sample_toptab_24R %>%
# mutate(Entrez_ID = as.character(Entrez_ID))
# ----------------- Map Entrez_ID to Gene Symbol -----------------
# gene_symbols2 <- AnnotationDbi::select(
# org.Hs.eg.db,
# keys = sample_toptab_24R$Entrez_ID,
# columns = c("SYMBOL"),
# keytype = "ENTREZID"
# )
# # ----------------- Join Back to Main Data -----------------
# Toptable_RUV_24R <- left_join(sample_toptab_24R, gene_symbols2, by = c("Entrez_ID" = "ENTREZID"))
# Toptable_RUV_24R %>% column_to_rownames(., var = "Entrez_ID")
# # ----------------- Save Annotated Output -----------------
# #I'll make these symbols into rownames later for my volcano plots
# write_csv(Toptable_RUV_24R, "data/DE/Toptable_RUV_24R.csv")
#
# Toptable_RUV_24R <- read.csv("data/DE/Toptable_RUV_24R.csv")
#
# ####D144R####
# #load in data
# sample_toptab_144R <- read_csv("C:/Users/emmap/RDirectory/Recovery_RNAseq/Recovery_5FU/data/new/DEGs/Toptable_V.D144R_k1.csv", show_col_types = FALSE)
# # ----------------- Ensure Entrez_ID is Present and in Character Format -----------------
# # Check column names
# print(colnames(sample_toptab_144R))
# # Rename if needed (adjust if the column name is not exactly 'Entrez_ID')
# colnames(sample_toptab_144R)[1] <- "Entrez_ID"
# # Convert Entrez_ID to character
# sample_toptab_144R <- sample_toptab_144R %>%
# mutate(Entrez_ID = as.character(Entrez_ID))
# # ----------------- Map Entrez_ID to Gene Symbol -----------------
# gene_symbols3 <- AnnotationDbi::select(
# org.Hs.eg.db,
# keys = sample_toptab_144R$Entrez_ID,
# columns = c("SYMBOL"),
# keytype = "ENTREZID"
# )
# # ----------------- Join Back to Main Data -----------------
# Toptable_RUV_144R <- left_join(sample_toptab_144R, gene_symbols3, by = c("Entrez_ID" = "ENTREZID"))
# Toptable_RUV_144R %>% column_to_rownames(., var = "Entrez_ID")
# # ----------------- Save Annotated Output -----------------
# #I'll make these symbols into rownames later for my volcano plots
# write_csv(Toptable_RUV_144R, "data/DE/Toptable_RUV_144R.csv")
#
# write.csv(Toptable_RUV_24T, "data/DE/Toptable_RUV_24T_final.csv")
# write.csv(Toptable_RUV_24R, "data/DE/Toptable_RUV_24R_final.csv")
# write.csv(Toptable_RUV_144R, "data/DE/Toptable_RUV_144R_final.csv")
Toptable_RUV_24T <- read_csv("data/DE/Toptable_RUV_24T_final.csv",
col_select = -1)
Toptable_RUV_24R <- read_csv("data/DE/Toptable_RUV_24R_final.csv",
col_select = -1)
Toptable_RUV_144R <- read_csv("data/DE/Toptable_RUV_144R_final.csv",
col_select = -1)
# save all of these toptables as R objects
# saveRDS(list(
# V.D24T_RUV = Toptable_RUV_24T,
# V.D24R_RUV = Toptable_RUV_24R,
# V.D144R_RUV = Toptable_RUV_144R
# ), file = "data/DE/Toptable_list_RUVk1_Symbols.RDS")
Toptable_list_RUVk1_symbols <- readRDS("data/DE/Toptable_list_RUVk1_Symbols.RDS")#Figure 2B - Barplot of DEG Percentages
###Proportion of DEGs in Gene Set RUVs corrected
#check the proportion of my genes in my gene set that are DEGs by timepoint
#make this into a pie chart or a bar graph
#read in my full gene set
filcpm_matrix <- readRDS("data/QC/filcpm_final_matrix_newind.RDS")
all_genes <- rownames(filcpm_matrix)
#convert Entrez_ID to Gene Symbols
all_genes_set <- mapIds(org.Hs.eg.db,
keys = all_genes,
column = "SYMBOL",
keytype = "ENTREZID",
multiVals = "first")
# Load DEGs Data (replaced with new set)
DOX_t0 <- read_csv("data/DE/Toptable_RUV_24T_final.csv",
col_select = -1)
DOX_t24 <- read_csv("data/DE/Toptable_RUV_24R_final.csv",
col_select = -1)
DOX_t144 <- read_csv("data/DE/Toptable_RUV_144R_final.csv",
col_select = -1)
# Extract Significant DEGs (change names to timepoint)
DEG_t0_RUV <- DOX_t0$Entrez_ID[DOX_t0$adj.P.Val < 0.05]
DEG_t24_RUV <- DOX_t24$Entrez_ID[DOX_t24$adj.P.Val < 0.05]
DEG_t144_RUV <- DOX_t144$Entrez_ID[DOX_t144$adj.P.Val < 0.05]
# saveRDS(DEG_t0_RUV, "data/DE/DEGs_sig_list_24T_RUVs_set.RDS")
# saveRDS(DEG_t24_RUV, "data/DE/DEGs_sig_list_24R_RUVs_set.RDS")
# saveRDS(DEG_t144_RUV, "data/DE/DEGs_sig_list_144R_RUVs_set.RDS")
#make a list of all of my significant DEGs
timepoint_DEGs_RUV <- list(
"24T" = DEG_t0_RUV,
"24R" = DEG_t24_RUV,
"144R" = DEG_t144_RUV
)
# Function to compute proportions
get_proportions_degs <- function(degs, total_genes) {
deg_count <- length(degs)
non_deg_count <- total_genes - deg_count
data.frame(
Category = c("DEGs", "Non-DEGs"),
Count = c(deg_count, non_deg_count),
Proportion = c(deg_count / total_genes, non_deg_count / total_genes)
)
}
# Compute proportions for each timepoint
prop_t0 <- get_proportions_degs(DEG_t0_RUV, length(all_genes_set))
prop_t24 <- get_proportions_degs(DEG_t24_RUV, length(all_genes_set))
prop_t144 <- get_proportions_degs(DEG_t144_RUV, length(all_genes_set))
# Combine for bar plot
prop_bar <- rbind(
cbind(Timepoint = "t0", prop_t0),
cbind(Timepoint = "t24", prop_t24),
cbind(Timepoint = "t144", prop_t144)
)
#pie chart for t0
ggplot(prop_t0, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
labs(title = "DEGs vs Non-DEGs (t0)") +
theme_void()
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#pie chart for t24
ggplot(prop_t24, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
labs(title = "DEGs vs Non-DEGs (t24)") +
theme_void()
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#pie chart for t144
ggplot(prop_t144, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
labs(title = "DEGs vs Non-DEGs (t144)") +
theme_void()
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#put the desired colors here for your pie charts
degs_prop_colors <- c("DEGs" = "red", "Non-DEGs" = "blue")
#labels for percentage based on proportion
prop_t0$Label <- paste0(round(prop_t0$Proportion * 100, 1), "")
prop_t24$Label <- paste0(round(prop_t24$Proportion * 100, 1), "")
prop_t144$Label <- paste0(round(prop_t144$Proportion * 100, 1), "")
#create each plot and assign to variables
plot_t0 <- ggplot(prop_t0, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
geom_text(aes(label = Label),
position = position_stack(vjust = 0.6),
color = "white",
size = 4) +
scale_fill_manual(values = degs_prop_colors) +
labs(title = "t0") +
theme_void()
plot_t24 <- ggplot(prop_t24, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
geom_text(aes(label = Label),
position = position_stack(vjust = 0.6),
color = "white",
size = 4) +
scale_fill_manual(values = degs_prop_colors) +
labs(title = "t24") +
theme_void()
plot_t144 <- ggplot(prop_t144, aes(x = "", y = Count, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
geom_text(aes(label = Label),
position = position_stack(vjust = 0.6),
color = "white",
size = 4) +
scale_fill_manual(values = degs_prop_colors) +
labs(title = "t144") +
theme_void()
#combine all three pie charts in a row
combined_prop_degs_plots <-
plot_t0 +
plot_t24 +
plot_t144 +
plot_layout(ncol = 3, nrow = 1, guides = "collect") +
plot_annotation(title = "Proportion of DEGs vs Non-DEGs")
#show the combined pie chart plot
combined_prop_degs_plots
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#combine all three pie charts in a column
combined_prop_degs_plots_col <-
plot_t0 +
plot_t24 +
plot_t144 +
plot_layout(ncol = 1, nrow = 3, guides = "collect") +
plot_annotation(title = "Proportion of DEGs vs Non-DEGs")
#show the combined pie chart plot in 1 column
combined_prop_degs_plots_col
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
####proportion bar plots####
#first factor the timepoints so that they are in the right order
prop_bar$Timepoint <- factor(prop_bar$Timepoint,
levels = c("t0", "t24", "t144"))
#percentage labels
prop_bar$Label <- paste0(round(prop_bar$Proportion * 100, 1), "")
#plot the bar plot
barplot_deg_perc <- ggplot(prop_bar, aes(x = Timepoint,
y = Proportion,
fill = Category)) +
geom_bar(stat = "identity",
position = "fill") +
geom_text(aes(label = Label),
position = position_fill(vjust = 0.5),
color = "white",
size = 4) +
scale_fill_manual(values = degs_prop_colors) +
labs(title = "Percentage of DEGs vs nonDEGs by Timepoint",
y = "Percentage of DEGs (%)",
x = "") +
scale_y_continuous(labels = c(0, 25, 50, 75, 100)) +
theme_custom()
print(barplot_deg_perc)
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#save this barplot
# save_plot(
# plot = barplot_deg_perc,
# filename = "DEGvsnonDEG_Barplot_update_EMP",
# folder = output_folder
# )Figure 2C - Venn Diagram of DEG Overlap
##Venn Diagrams of DEG Overlap after RUVs Correction k=1
#plot a venn diagram with all of your conditions from your toptables
# Load DEGs Data (already read in above)
# DOX_t0 <- read_csv("data/DE/Toptable_RUV_24T_final.csv",
# col_select = -1)
# DOX_t24 <- read_csv("data/DE/Toptable_RUV_24R_final.csv",
# col_select = -1)
# DOX_t144 <- read_csv("data/DE/Toptable_RUV_144R_final.csv",
# col_select = -1)
# Extract Significant DEGs (change names to timepoint)
# DEG_t0_RUV <- DOX_t0$Entrez_ID[DOX_t0$adj.P.Val < 0.05]
# DEG_t24_RUV <- DOX_t24$Entrez_ID[DOX_t24$adj.P.Val < 0.05]
# DEG_t144_RUV <- DOX_t144$Entrez_ID[DOX_t144$adj.P.Val < 0.05]
venn_test_RUV <- list(DEG_t0_RUV, DEG_t24_RUV, DEG_t144_RUV)
ggVennDiagram(
venn_test_RUV,
category.names = c("DOX_t0", "DOX_t24", "DOX_t144")
) + ggtitle("DXR Specific and Shared DEGs RUVs")+
theme_custom() +
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
####I want to make this proportional
#use the eulerr package to do so
venn_prop_RUV <- euler(list(
"t0" = DEG_t0_RUV,
"t24" = DEG_t24_RUV,
"t144" = DEG_t144_RUV
))
plot(venn_prop_RUV,
fills = list(fill = c("#263CA5", "#5B8FD1", "#89C5E5"),
alpha = 1),
labels = list(font = 4),
quantities = list(cex = 0.75),
main = "")
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
Load in DEGs
#Load DEGs Data
#make a list of all of the genes in this set so I can plot the logFC in other sets
# Load DEGs Data (already read in above)
# DOX_t0 <- read_csv("data/DE/Toptable_RUV_24T_final.csv",
# col_select = -1)
# DOX_t24 <- read_csv("data/DE/Toptable_RUV_24R_final.csv",
# col_select = -1)
# DOX_t144 <- read_csv("data/DE/Toptable_RUV_144R_final.csv",
# col_select = -1)
DOX_24T_R <- read_csv("data/DE/Toptable_RUV_24T.csv")
DOX_24R_R <- read_csv("data/DE/Toptable_RUV_24R_final.csv")
DOX_144R_R <- read_csv("data/DE/Toptable_RUV_144R_final.csv")
#plot those full gene sets in logFC
D24T_DEGs_R <- DOX_t0$Entrez_ID[DOX_t0$adj.P.Val < 0.05]
length(D24T_DEGs_R)[1] 9420#9420 DEGs
D24R_DEGs_R <- DOX_t24$Entrez_ID[DOX_t24$adj.P.Val < 0.05]
length(D24R_DEGs_R)[1] 7212#7168 DEGs
D144R_DEGs_R <- DOX_t144$Entrez_ID[DOX_t144$adj.P.Val < 0.05]
length(D144R_DEGs_R)[1] 660#660 DEGs
#now that I have the full list of genes, I want to plot the logFC across conditions
#Combine the toptables I have from pairwise analysis into a single dataframe
d24_toptable_dxr_1 <- Toptable_RUV_24T %>%
mutate(Time = "t0")
d24r_toptable_dxr_1 <- Toptable_RUV_24R %>%
mutate(Time = "t24")
d144r_toptable_dxr_1 <- Toptable_RUV_144R %>%
mutate(Time = "t144")
combined_toptables_dxr_RUV <- bind_rows(
d24_toptable_dxr_1,
d24r_toptable_dxr_1,
d144r_toptable_dxr_1)
# saveRDS(combined_toptables_dxr_RUV, "data/DE/combined_toptables_dxr_RUV.RDS")Figure 2D - Scatterplots logFC Timepoint Comparisons
# Define the timepoints from your data
timepoints <- c("t0", "t24", "t144")
comparison_order <- c("t0 vs t24", "t24 vs t144", "t0 vs t144")
# Make all pairwise combinations
pairs_df <- t(combn(timepoints, 2)) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
setNames(c("time_x", "time_y"))
logFC_wide_RUV <- combined_toptables_dxr_RUV %>%
mutate(absFC = abs(logFC)) %>%
dplyr::select(Entrez_ID, Time, logFC) %>%
pivot_wider(names_from = Time, values_from = logFC) %>%
drop_na() %>%
column_to_rownames("Entrez_ID")
# Function to prepare scatter data for each pair
prepare_pair_data <- function(time_x, time_y, df) {
rho <- cor(df[[time_x]], df[[time_y]], method = "spearman")
df %>%
mutate(
x_val = .data[[time_x]],
y_val = .data[[time_y]],
comparison = paste0(time_x, " vs ", time_y),
rho_label = paste0("rho = ", round(rho, 2)),
x_lab = paste0("log2FC", time_x),
y_lab = paste0("log2FC", time_y)
)
}
comparison_colors <- c(
"t0 vs t24" = "#4167BD",
"t24 vs t144" = "#72AEE0",
"t0 vs t144" = "#5780C5"
)
# Build combined plotting dataset
scatter_df <- map2_df(pairs_df$time_x, pairs_df$time_y,
~prepare_pair_data(.x, .y, logFC_wide_RUV)) %>%
mutate(comparison = factor(comparison, levels = comparison_order))
facet_labels <- scatter_df %>%
distinct(comparison, x_lab, y_lab) %>%
mutate(label = paste0(comparison, "\n", x_lab, " | ", y_lab)) %>%
dplyr::select(comparison, label) %>%
deframe()
#split data by comparison
split_data <- split(scatter_df, scatter_df$comparison)
make_scatter <- function(df_sub) {
comp <- df_sub$comparison[1]
ggplot(df_sub, aes(x = x_val, y = y_val)) +
geom_point_rast(alpha = 0.5, raster.dpi = 300, color = comparison_colors[comp]) +
geom_smooth(method = "lm", se = FALSE, linetype = "dashed", color = "black") +
geom_text(
data = df_sub %>% distinct(comparison, rho_label),
aes(x = -Inf, y = Inf, label = rho_label),
inherit.aes = FALSE,
hjust = -0.1, vjust = 1.5,
size = 4
) +
coord_fixed(xlim = c(-9,9), ylim = c(-9,9), ratio = 1) +
labs(
x = unique(df_sub$x_lab),
y = unique(df_sub$y_lab)
) +
theme_custom() +
theme(
axis.line = element_line(linewidth = 1,
color = "black"),
axis.ticks.length = unit(0.05, "in"),
axis.text = element_text(color = "black"),
panel.background = element_blank(),
panel.border = element_blank(),
legend.position = "none"
)
}
#make plot
plot_list <- lapply(split_data, make_scatter)
#wrap all three together to get the final plot
scatterplot_fig2d_wide <- wrap_plots(plot_list, ncol = length(plot_list))
scatterplot_fig2d_tall <- wrap_plots(plot_list, nrow = length(plot_list))
scatterplot_fig2d_wide
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
scatterplot_fig2d_tall
| Version | Author | Date |
|---|---|---|
| a967d53 | emmapfort | 2025-12-09 |
#save plot to a pdf and to a png
# ggsave("Fig2D_Scatterplot_fixedlims_EMP_251121.pdf", scatterplot_fig2d_wide, width = 8, height = 6, device = cairo_pdf, path = "C:/Users/emmap/OneDrive/Desktop/DXR Manuscript Materials/Fig pdfs")
# ggsave("Fig2D_Scatterplot_fixedlims_EMP_251121.png", scatterplot_fig2d_wide, width = 12, height = 4, dpi = 300, type = "cairo", path = "C:/Users/emmap/OneDrive/Desktop/DXR Manuscript Materials/Fig pdfs")Supplementary Figure 4 - Volcano Plots of RUVs DEGs
#make a function to generate volcano plots + add gene numbers
#ensure the gene symbols are in a col so I can plot names of top 15
generate_volcano_plot_RUV <- function(toptable, title) {
#make significance labels
toptable$Significance <- "Not Significant"
toptable$Significance[toptable$logFC > 0 & toptable$adj.P.Val < 0.05] <- "Upregulated"
toptable$Significance[toptable$logFC < 0 & toptable$adj.P.Val < 0.05] <- "Downregulated"
#enforce correct factor order
toptable$Significance <- factor(
toptable$Significance,
levels = c("Downregulated", "Not Significant", "Upregulated")
)
#add number of genes for each significance label
upgenes <- toptable %>%
filter(Significance == "Upregulated") %>%
nrow()
nsgenes <- toptable %>%
filter(Significance == "Not Significant") %>%
nrow()
downgenes <- toptable %>%
filter(Significance == "Downregulated") %>%
nrow()
#FIX: correct legend labels
legend_lab <- c(
"Upregulated" = paste0("up = ", upgenes),
"Not Significant" = paste0("ns = ", nsgenes),
"Downregulated" = paste0("down = ", downgenes)
)
#FIX: correct legend colors
legend_col <- c(
"Upregulated" = "blue",
"Not Significant" = "gray",
"Downregulated" = "red"
)
#generate volcano plot w/ legend
p <- ggplot(toptable, aes(x = logFC,
y = -log10(P.Value),
color = Significance)) +
geom_point_rast(alpha = 0.6, size = 2) + #rasterize
scale_color_manual(values = legend_col,
labels = legend_lab) +
xlim(-10, 10) +
ylim(0, 25) +
labs(title = title,
x = expression("log"[2]*"FC"),
y = expression("-log"[10]*"P-value")) +
theme_custom() +
theme(legend.position = "bottom")
return(p)
}
#generate volcano plots across each comparison
volcano_plots_RUV <- list(
"Volcano_Supp4_1_EMP" = generate_volcano_plot_RUV(Toptable_RUV_24T, "t0"),
"Volcano_Supp4_2_EMP" = generate_volcano_plot_RUV(Toptable_RUV_24R, "t24"),
"Volcano_Supp4_3_EMP" = generate_volcano_plot_RUV(Toptable_RUV_144R, "t144")
)
# Display each volcano plot
for (plot_name in names(volcano_plots_RUV)) {
print(volcano_plots_RUV[[plot_name]])
}
#save all volcano plots
for (plot_name in names(volcano_plots_RUV)) {
save_plot(
filename = paste0(plot_name, "_EMP.pdf"),
plot = volcano_plots_RUV[[plot_name]],
height = 4,
width = 4,
folder = output_folder
)
}
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22000)
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] ggrastr_1.0.2 ggVennDiagram_1.5.2
[3] org.Hs.eg.db_3.20.0 AnnotationDbi_1.68.0
[5] eulerr_7.0.2 patchwork_1.3.0
[7] RUVSeq_1.40.0 edgeR_4.4.0
[9] limma_3.62.1 EDASeq_2.40.0
[11] ShortRead_1.64.0 GenomicAlignments_1.42.0
[13] SummarizedExperiment_1.36.0 MatrixGenerics_1.18.1
[15] matrixStats_1.5.0 Rsamtools_2.22.0
[17] GenomicRanges_1.58.0 Biostrings_2.74.0
[19] GenomeInfoDb_1.42.3 XVector_0.46.0
[21] IRanges_2.40.0 S4Vectors_0.44.0
[23] BiocParallel_1.40.0 Biobase_2.66.0
[25] BiocGenerics_0.52.0 ggfortify_0.4.17
[27] ggrepel_0.9.6 readxl_1.4.5
[29] lubridate_1.9.4 forcats_1.0.0
[31] stringr_1.5.1 dplyr_1.1.4
[33] purrr_1.0.4 readr_2.1.5
[35] tidyr_1.3.1 tibble_3.2.1
[37] ggplot2_3.5.2 tidyverse_2.0.0
[39] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.2 later_1.4.2 BiocIO_1.16.0
[4] bitops_1.0-9 filelock_1.0.3 R.oo_1.27.1
[7] cellranger_1.1.0 polyclip_1.10-7 XML_3.99-0.18
[10] lifecycle_1.0.4 httr2_1.1.2 pwalign_1.2.0
[13] rprojroot_2.0.4 processx_3.8.6 lattice_0.22-6
[16] vroom_1.6.5 MASS_7.3-61 magrittr_2.0.3
[19] sass_0.4.10 rmarkdown_2.29 jquerylib_0.1.4
[22] yaml_2.3.10 httpuv_1.6.16 DBI_1.2.3
[25] RColorBrewer_1.1-3 abind_1.4-8 zlibbioc_1.52.0
[28] R.utils_2.13.0 RCurl_1.98-1.17 rappdirs_0.3.3
[31] git2r_0.36.2 GenomeInfoDbData_1.2.13 codetools_0.2-20
[34] DelayedArray_0.32.0 xml2_1.3.8 tidyselect_1.2.1
[37] UCSC.utils_1.2.0 farver_2.1.2 BiocFileCache_2.14.0
[40] jsonlite_2.0.0 systemfonts_1.2.2 polylabelr_0.3.0
[43] tools_4.4.2 progress_1.2.3 ragg_1.4.0
[46] Rcpp_1.0.14 glue_1.8.0 gridExtra_2.3
[49] SparseArray_1.6.0 xfun_0.52 mgcv_1.9-1
[52] withr_3.0.2 fastmap_1.2.0 latticeExtra_0.6-30
[55] callr_3.7.6 digest_0.6.37 timechange_0.3.0
[58] R6_2.6.1 textshaping_1.0.1 colorspace_2.1-1
[61] Cairo_1.6-5 jpeg_0.1-11 biomaRt_2.62.1
[64] RSQLite_2.3.9 R.methodsS3_1.8.2 generics_0.1.4
[67] rtracklayer_1.66.0 prettyunits_1.2.0 httr_1.4.7
[70] S4Arrays_1.6.0 whisker_0.4.1 pkgconfig_2.0.3
[73] gtable_0.3.6 blob_1.2.4 hwriter_1.3.2.1
[76] htmltools_0.5.8.1 scales_1.4.0 png_0.1-8
[79] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
[82] rjson_0.2.23 nlme_3.1-166 curl_6.0.1
[85] cachem_1.1.0 parallel_4.4.2 vipor_0.4.7
[88] restfulr_0.0.15 pillar_1.10.2 grid_4.4.2
[91] vctrs_0.6.5 promises_1.3.2 dbplyr_2.5.0
[94] beeswarm_0.4.0 evaluate_1.0.3 GenomicFeatures_1.58.0
[97] cli_3.6.3 locfit_1.5-9.12 compiler_4.4.2
[100] rlang_1.1.6 crayon_1.5.3 labeling_0.4.3
[103] interp_1.1-6 aroma.light_3.36.0 ps_1.9.1
[106] getPass_0.2-4 fs_1.6.6 ggbeeswarm_0.7.2
[109] stringi_1.8.7 deldir_2.0-4 Matrix_1.7-1
[112] hms_1.1.3 bit64_4.5.2 KEGGREST_1.46.0
[115] statmod_1.5.0 memoise_2.0.1 bslib_0.9.0
[118] bit_4.5.0