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plots.Rmd
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plots.Rmd
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---
title: "Aging TE correlations"
author: "Nogayhan Seymen"
date: "08/06/2023"
output:
html_document:
code_folding: hide
highlight: zenburn
number_sections: yes
theme: yeti
toc: yes
---
```{=html}
<style type="text/css">
.main-container {
max-width: 90%;
margin-left: 150px;
margin-right: auto;
}
</style>
```
```{r setup, include=FALSE}
library(ComplexHeatmap)
library(ggsci)
library(readxl)
library(ggplot2)
library(ggpmisc)
library(ggpubr)
library(ggrepel)
library(dplyr)
library(PerformanceAnalytics)
library(pals)
library(tidyr)
library(grid)
library(gridExtra)
library(plyr)
library(GGally)
library(GSVA)
library(limma)
library(GSEABase)
library(org.Hs.eg.db)
library(fmsb)
```
# PLOT FUNCTIONS
```{r,echo=F, eval=T, cache=F, message=F, warning=FALSE}
# Define TE types for annotation
te_anno <- data.frame(Feature = c("LTR", "LINE", "SINE",
"L1", "Alu",
"ERV1", "ERVL", "L2", "ERVL-MaLR",
"MIR", "ERVK"),
Type = c(rep("Class", 3), rep("Family", 8)))
te_anno_colors <- list(Type = c("Class" = "#9B23E8", "Family" = "#F7730C"))
# Function for reading all excel sheets
read_excel_allsheets <- function(filename, tibble = FALSE) {
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
gene_sets <- read_excel_allsheets("../Data/gene_signatures_230607.xlsx")
lengths <- unlist(lapply(gene_sets, FUN = function(x){lapply(x, FUN = function(y){length(y[!is.na(y)])})}))
# Initiate list for age histogram
age_list <- list()
# Initiate data frame for correlation bar charts
r_bars <- data.frame()
p_bars <- data.frame()
r_bars_fam <- data.frame()
p_bars_fam <- data.frame()
# Function for creating correlation matrices along with p-vals
corrTabs <- function(mat1, mat2){
inf_df <- cbind(mat1, mat2)
inf_df <- mutate_all(inf_df, function(x)as.numeric(x))
corMat <- list()
corMat$P <- data.frame()
corMat$r <- data.frame()
mat1_feats <- colnames(mat1)
mat2_feats <- colnames(mat2)
# change Age from column to row
mat1_feats <- mat1_feats[mat1_feats != "Age"]
mat2_feats <- c(mat2_feats, "Age")
inf_df <- t(inf_df)
for (feat1 in mat1_feats){
for (feat2 in mat2_feats) {
corMat$P[feat1, feat2] <- tryCatch({
round(cor.test(as.numeric(inf_df[feat1,]), as.numeric(inf_df[feat2,]), method = "pearson")$p.value, 3)
},
error = function(err) {return(NA)}
)
corMat$r[feat1, feat2] <- tryCatch({
round(cor.test(as.numeric(inf_df[feat1,]), as.numeric(inf_df[feat2,]), method = "pearson")$estimate, 2)
},
error = function(err) {return(NA)}
)
}
}
return(corMat)
}
# Function for creating correlation matrices along with p-vals all vs. all
corrTabs.all <- function(mat, sgn){
sgn_df <- dplyr::bind_rows(sgn)
sgn_df <- t(sgn_df)
colnames(sgn_df) <- sgn_df[1,]
sgn_df <- sgn_df[-1,]
inf_df <- cbind(mat, sgn_df)
inf_df <- mutate_all(inf_df, function(x)as.numeric(x))
corMat <- list()
corMat$P <- data.frame()
corMat$r <- data.frame()
inf_df <- t(inf_df)
for (feat1 in rownames(inf_df)){
for (feat2 in rownames(inf_df)) {
corMat$P[feat1, feat2] <- tryCatch({
round(cor.test(as.numeric(inf_df[feat1,]), as.numeric(inf_df[feat2,]), method = "pearson")$p.value, 3)
},
error = function(err) {return(NA)}
)
corMat$r[feat1, feat2] <- tryCatch({
round(cor.test(as.numeric(inf_df[feat1,]), as.numeric(inf_df[feat2,]), method = "pearson")$estimate, 2)
},
error = function(err) {return(NA)}
)
}
}
return(corMat)
}
te.corr.all <- function(cohort, type = "all"){
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sign_colors <- list(`Gene Set` = c(setNames(pals::alphabet(n = length(names(sign_scores))+1),
c("Age",names(sign_scores)))))
# Add age info to age list
# age_list[[cohort]] <<- te_exp$Age
features <- c()
corMat <- corrTabs.all(te_exp %>% dplyr::select("Age", "LTR", "LINE", "SINE",
"L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR",
"MIR", "ERVK"),
sign_scores)
# Convert NA's to insignificant which would show white in the plot
# If there is one column, complete.cases removes column name
corMat$P[is.na(corMat$P)] <- 1
corMat$r[is.na(corMat$r)] <- 0
features <- c(features, colnames(corMat$P))
types <- c("Age", rep("Class", 3), rep("Family", 8), unlist(lapply(names(sign_scores), FUN = function(x){rep(x, dim(sign_scores[[x]])[1])})))
# Remove duplicate Age annotations and fix gene set
anno_df <- data.frame(Feature = features, Type = types)
te.plot.all(corMat$P, corMat$r, anno_df, sign_colors, sign_scores, te_exp$Age, type, cohort)
}
col_fun = circlize::colorRamp2(c(-1, 0, 1), c("blue", "white", "red"))
te.corr <- function(cohort){
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sign_colors <- list(`Gene Set` = c(setNames(pals::alphabet(n = length(names(sign_scores))+1),
c("Age",names(sign_scores)))))
# Add age info to age list
age_list[[cohort]] <<- te_exp$Age
p_list <- list()
r_list <- list()
features <- c()
types <- c()
for (s in names(sign_scores)){
# Assign column 1 as row names and remove it
rownames(sign_scores[[s]]) <- sign_scores[[s]][,1]
sign_scores[[s]] <- sign_scores[[s]][,-1]
# Calculate correlation
corMat <- corrTabs(te_exp %>% dplyr::select("Age", "LTR", "LINE", "SINE",
"L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR",
"MIR", "ERVK"),
t(sign_scores[[s]]))
# Convert NA's to insignificant which would show white in the plot
# If there is one column, complete.cases removes column name
corMat$P[is.na(corMat$P)] <- 1
corMat$r[is.na(corMat$r)] <- 0
p_list[[s]] <- corMat$P
r_list[[s]] <- corMat$r
features <- c(features, colnames(corMat$P))
types <- c(types, rep(s, length(colnames(corMat$P))))
}
p_corr <- dplyr::bind_cols(p_list)
r_corr <- dplyr::bind_cols(r_list)
# Remove duplicate Age columns
p_corr <- p_corr %>%
dplyr::rename(Age = colnames(p_corr)[grepl(pattern = "Age", colnames(p_corr))][1]) %>%
dplyr::select(!colnames(p_corr)[grepl(pattern = "Age", colnames(p_corr))][-1])
r_corr <- r_corr %>%
dplyr::rename(Age = colnames(r_corr)[grepl(pattern = "Age", colnames(r_corr))][1]) %>%
dplyr::select(!colnames(r_corr)[grepl(pattern = "Age", colnames(r_corr))][-1])
# Remove duplicate Age annotations and fix gene set
anno_df <- data.frame(Feature = features, Type = types)
anno_df <- anno_df[-which(grepl("Age", anno_df$Feature))[-1],]
anno_df[anno_df$Feature == "Age", "Type"] <- "Age"
# add Age
##### Change if order of gene sets change !!!!!!!!!!!!
anno_df$Lengths <- append(lengths, 1, after = 10)
te.plot(p_corr, r_corr, anno_df, sign_colors, sign_scores, te_exp$Age)
remove(p_corr)
remove(r_corr)
}
te.plot <- function(ps, rs, anno, sign_colors, sign_scores, ages){
sign_df <- dplyr::bind_rows(sign_scores, .id = '')
# Add age row
sign_df["Age",] <- c("Age", ages)
# Remove NA and duplicate rows
# sign_df <- sign_df[complete.cases(sign_df),]
# sign_df <- sign_df[!duplicated(sign_df),]
colnames(sign_df)[1:2] <- c("Type", "Gene_Set")
# sign_df <- sign_df[,-1]
sign_df <- transform(sign_df, SD=apply(sign_df,1, sd, na.rm = TRUE))
sign_df["Age","SD"] <- sign_df["Age","SD"]/100
sign_df_alt <- sign_df[match(anno$Feature, rownames(sign_df)),]
alt_anno <- data.frame(Type = t(te_anno$Type)[order(match(te_anno$Feature,rownames(rs)))])
rownames(alt_anno) <- rownames(rs)
p <- ComplexHeatmap::Heatmap(t(rs), col = col_fun,
rect_gp = gpar(col = "white", lwd = 2),
cell_fun = function(j, i, x, y, width, height, fill) {
if(t(ps)[i, j] < 0.05) {
grid.text("*", x, y)
}
},
heatmap_legend_param = list(
title = "correlation (r)", direction = "horizontal"
),
row_names_gp = grid::gpar(fontsize = 6),
row_split = anno$Type,
column_split = alt_anno$Type,
show_row_dend = F,
row_title = NULL,
column_title = NULL,
left_annotation = rowAnnotation(`Gene Set` = anno$Type, col = sign_colors),
bottom_annotation = HeatmapAnnotation(`Type` = alt_anno$Type, col = te_anno_colors,
show_annotation_name = F),
right_annotation = rowAnnotation(SD = anno_barplot(sign_df_alt$SD),
Length = anno_text(anno$Lengths, gp = gpar(fontsize = 5))),
show_column_names = T
)
draw(p, heatmap_legend_side = "left", annotation_legend_side = "left")
}
te.plot.all <- function(ps, rs, anno, sign_colors, sign_scores, ages, type, cohort){
if (type == "all"){
# Append r and p values to p_bars and r_bars
r_bars <<- rbind(r_bars, cbind(rs[c("LINE", "SINE", "LTR"),], Cohort = rep(cohort, 3), Class = c("LINE", "SINE", "LTR")))
p_bars <<- rbind(p_bars, cbind(ps[c("LINE", "SINE", "LTR"),], Cohort = rep(cohort, 3), Class = c("LINE", "SINE", "LTR")))
r_bars_fam <<- rbind(r_bars_fam, cbind(rs[c("L1", "L2", "Alu", "ERV1", "ERVL", "ERVL-MaLR", "ERVK", "MIR"),], Cohort = rep(cohort, 8),
Family = c("L1", "L2", "Alu", "ERV1", "ERVL", "ERVL-MaLR", "ERVK", "MIR")))
p_bars_fam <<- rbind(p_bars_fam, cbind(ps[c("L1", "L2", "Alu", "ERV1", "ERVL", "ERVL-MaLR", "ERVK", "MIR"),], Cohort = rep(cohort, 8),
Family = c("L1", "L2", "Alu", "ERV1", "ERVL", "ERVL-MaLR", "ERVK", "MIR")))
p <- ComplexHeatmap::Heatmap(t(rs), col = col_fun,
rect_gp = gpar(col = "white", lwd = 2),
cell_fun = function(j, i, x, y, width, height, fill) {
if(t(ps)[i, j] < 0.05) {
grid.text("*", x, y)
}
},
heatmap_legend_param = list(
title = "correlation (r)", direction = "horizontal"
),
row_names_gp = grid::gpar(fontsize = 6),
show_row_dend = F,
row_title = NULL,
column_title = NULL,
show_column_names = T
)
draw(p, heatmap_legend_side = "left", annotation_legend_side = "left")
} else if (type == "no_class"){
ps <- ps[!(rownames(ps) %in% c("LTR", "LINE", "SINE")), !(colnames(ps) %in% c("LTR", "LINE", "SINE"))]
rs <- rs[!(rownames(rs) %in% c("LTR", "LINE", "SINE")), !(colnames(rs) %in% c("LTR", "LINE", "SINE"))]
p <- ComplexHeatmap::Heatmap(t(rs), col = col_fun,
rect_gp = gpar(col = "white", lwd = 2),
cell_fun = function(j, i, x, y, width, height, fill) {
if(t(ps)[i, j] < 0.05) {
grid.text("*", x, y)
}
},
heatmap_legend_param = list(
title = "correlation (r)", direction = "horizontal"
),
row_names_gp = grid::gpar(fontsize = 6),
show_row_dend = F,
row_title = NULL,
column_title = NULL,
show_column_names = T
)
draw(p, heatmap_legend_side = "left", annotation_legend_side = "left")
} else if (type == "no_family"){
ps <- ps[!(rownames(ps) %in% c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK")),
!(colnames(ps) %in% c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK"))]
rs <- rs[!(rownames(rs) %in% c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK")),
!(colnames(rs) %in% c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK"))]
p <- ComplexHeatmap::Heatmap(t(rs), col = col_fun,
rect_gp = gpar(col = "white", lwd = 2),
cell_fun = function(j, i, x, y, width, height, fill) {
if(t(ps)[i, j] < 0.05) {
grid.text("*", x, y)
}
},
heatmap_legend_param = list(
title = "correlation (r)", direction = "horizontal"
),
row_names_gp = grid::gpar(fontsize = 6),
show_row_dend = F,
row_title = NULL,
column_title = NULL,
show_column_names = T
)
draw(p, heatmap_legend_side = "left", annotation_legend_side = "left")
} else {
print("Valid types are 'all', 'no_class' and 'no_family'!")
}
}
##### CUSTOM GGPAIRS FUNCTIONS #####
# Set lower half function for ggpairs
lowerFn <- function(data, mapping, method = "lm", point.size = 0.5, ...) {
p <- ggplot(data = data, mapping = mapping) +
geom_point(colour = "black", size = point.size) +
geom_smooth(method = method, color = "red", ...) +
theme_pubr()
p
}
upperFn <- function(data, mapping, method="p", use="pairwise", ...){
# grab data
x <- eval_data_col(data, mapping$x)
y <- eval_data_col(data, mapping$y)
# calculate correlation
corr <- cor(x, y, method=method, use=use)
# calculate colour based on correlation value
# Here I have set a correlation of minus one to blue,
# zero to white, and one to red
# Change this to suit: possibly extend to add as an argument of `my_fn`
colFn <- colorRampPalette(c("dodgerblue3", "white", "firebrick"))
fill <- colFn(100)[findInterval(corr, seq(-1, 1, length=100))]
ggally_cor_alt(data = data, mapping = mapping, size = 5, family = "Helvetica", color = "black", title = NULL, ...) +
theme_pubr() +
theme(panel.background = element_rect(fill=fill))
}
ggally_cor_alt <- function(
data,
mapping,
...,
stars = TRUE,
method = "pearson",
use = "complete.obs",
display_grid = FALSE,
digits = 3,
title_args = list(...),
group_args = list(...),
justify_labels = "right",
align_percent = 0.5,
title = "",
alignPercent = warning("deprecated. Use `align_percent`"),
displayGrid = warning("deprecated. Use `display_grid`")
) {
if (!missing(alignPercent)) {
warning("`alignPercent` is deprecated. Please use `align_percent` if alignment still needs to be adjusted")
align_percent <- alignPercent
}
if (!missing(displayGrid)) {
warning("`displayGrid` is deprecated. Please use `display_grid`")
display_grid <- displayGrid
}
na.rm <-
if (missing(use)) {
# display warnings
NA
} else {
(use %in% c("complete.obs", "pairwise.complete.obs", "na.or.complete"))
}
ggally_statistic(
data = data,
mapping = mapping,
na.rm = na.rm,
align_percent = align_percent,
display_grid = display_grid,
title_args = title_args,
group_args = group_args,
justify_labels = justify_labels,
justify_text = "left",
sep = "",
title = title,
text_fn = function(x, y) {
corObj <- stats::cor.test(x, y, method = method, use = use)
# make sure all values have X-many decimal places
cor_est <- as.numeric(corObj$estimate)
cor_txt <- formatC(cor_est, digits = digits, format = "f")
# if stars should be added
if (isTRUE(stars)) {
cor_txt <- stringr::str_c(
cor_txt,
signif_stars(corObj$p.value)
)
}
cor_txt
}
)
}
#####
PA.all <- function(cohort, type = "all"){
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sign_colors <- list(`Gene Set` = c(setNames(pals::alphabet(n = length(names(sign_scores))+1),
c("Age",names(sign_scores)))))
sgn_df <- dplyr::bind_rows(sign_scores)
sgn_df <- t(sgn_df)
colnames(sgn_df) <- sgn_df[1,]
sgn_df <- sgn_df[-1,]
if (type == "all"){
inf_df <- cbind(te_exp %>% dplyr::select("Age", "LTR", "LINE", "SINE",
"L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR"),
sgn_df)
inf_df <- inf_df[,sapply(inf_df, FUN = function(x){all(!is.na(x))})] # drop all NA columns
inf_df <- mutate_all(inf_df, function(x)as.numeric(x))
col_ord <- c("Age", "LTR", "LINE", "SINE", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR", "IFN-I", "Inflammatory cytokines",
"Inflammatory chemokines","Inflammaging", "SASP", "Senescence")
inf_df <- inf_df[,col_ord]
colnames(inf_df) <- gsub(" ", "\n", colnames(inf_df))
colnames(inf_df) <- gsub("ERVL-", "ERVL-\n", colnames(inf_df))
pdf(file = paste0("../Figures/images/", cohort,"_pairs_all.pdf"),
width = 400*0.0394,
height = 400*0.0394)
g <- GGally::ggpairs(inf_df,
upper = list(continuous = wrap(ggally_cor_alt,
method = 'pearson')),
lower = list(continuous = wrap(lowerFn)),
diag = list(continuous = function(...) ggally_densityDiag(...)+theme_pubr())) +
theme(strip.text.x = element_blank(),
strip.text.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
print(g)
dev.off()
} else if (type == "family"){
inf_df <- cbind(te_exp %>% dplyr::select("Age", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR"),
sgn_df)
inf_df <- inf_df[,sapply(inf_df, FUN = function(x){all(!is.na(x))})]
inf_df <- mutate_all(inf_df, function(x)as.numeric(x))
col_ord <- c("Age", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR", "IFN-I", "Inflammatory cytokines",
"Inflammatory chemokines","Inflammaging", "SASP", "Senescence")
inf_df <- inf_df[,col_ord]
colnames(inf_df) <- gsub(" ", "\n", colnames(inf_df))
colnames(inf_df) <- gsub("ERVL-", "ERVL-\n", colnames(inf_df))
pdf(file = paste0("../Figures/images/", cohort,"_pairs_family.pdf"),
width = 400*0.0394,
height = 400*0.0394)
g <- GGally::ggpairs(inf_df,
upper = list(continuous = wrap(upperFn,
method = 'pearson')),
lower = list(continuous = wrap(lowerFn)),
diag = list(continuous = function(...) ggally_densityDiag(...)+theme_pubr())) +
theme(strip.text.x = element_blank(),
strip.text.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
print(g)
dev.off()
} else if (type == "class"){
inf_df <- cbind(te_exp %>% dplyr::select("Age", "LTR", "LINE", "SINE"),
sgn_df)
inf_df <- inf_df[,sapply(inf_df, FUN = function(x){all(!is.na(x))})]
inf_df <- mutate_all(inf_df, function(x)as.numeric(x))
col_ord <- c("Age", "LTR", "LINE", "SINE", "Inflammatory cytokines",
"Inflammatory chemokines","Inflammaging", "SASP", "Senescence")
inf_df <- inf_df[,col_ord]
colnames(inf_df) <- gsub(" ", "\n", colnames(inf_df))
colnames(inf_df) <- gsub("ERVL-", "ERVL-\n", colnames(inf_df))
pdf(file = paste0("../Figures/images/", cohort,"_pairs_class.pdf"),
width = 400*0.0394,
height = 400*0.0394)
g <- GGally::ggpairs(inf_df,
upper = list(continuous = wrap(ggally_cor_alt,
method = 'pearson')),
lower = list(continuous = wrap(lowerFn)),
diag = list(continuous = function(...) ggally_densityDiag(...)+theme_pubr())) +
theme(strip.text.x = element_blank(),
strip.text.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank())
print(g)
dev.off()
} else {
print("Valid types are 'all', 'no_class' and 'no_family'!")
}
}
scatter.all <- function(cohort, type = "all", signatures = c("SASP", "Senescence", "Inflammaging")){
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sgn_df <- dplyr::bind_rows(sign_scores)
sgn_df <- t(sgn_df)
colnames(sgn_df) <- sgn_df[1,]
sgn_df <- sgn_df[-1,]
sgn_df <- dplyr::mutate_all(as.data.frame(sgn_df), function(x)as.numeric(x))
colnames(sgn_df) <- gsub(" ", "\n", colnames(sgn_df))
# Filter signatures
sgn_df <- sgn_df[,signatures]
if (type == "all"){
cor_df <- cbind(te_exp[,c("LTR", "LINE", "SINE", "L1", "Alu", "ERV1",
"ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK")], sgn_df)
cor_long <- tidyr::pivot_longer(cor_df, cols = c("LTR", "LINE", "SINE", "L1", "Alu", "ERV1",
"ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK"))
colnames(cor_long)[(length(colnames(cor_long))-1):length(colnames(cor_long))] <- c("TE name", "TE expression")
cor_longer <- tidyr::pivot_longer(cor_long, cols = colnames(cor_df)[12:length(colnames(cor_df))])
colnames(cor_longer)[(length(colnames(cor_longer))-1):length(colnames(cor_longer))] <- c("Signature name", "Score")
cor_longer$Cohort <- cohort
cor_longer$`TE name` <- factor(cor_longer$`TE name`,
levels = c("LTR", "LINE", "SINE", "L1", "L2",
"ERV1", "ERVL", "ERVL-MaLR", "ERVK",
"Alu", "MIR"))
pdf(file = paste0("../Figures/images/", cohort,"_scatter_all.pdf"),
width = 88*0.0394,
height = 220*0.0394)
g <- ggplot(data = cor_longer, aes(x = Score, y = `TE expression`)) +
geom_point(size = 0.5) +
geom_smooth(method = "lm", se=FALSE, color="red", formula = y ~ x, size = 0.5) +
facet_grid(facets = `TE name` ~ `Signature name`, scales = "free", switch = "both") +
theme_pubr() +
theme(axis.text.x = element_text(size = 5, family = "Helvetica"),
axis.text.y = element_text(size = 5, family = "Helvetica"),
axis.title.x = element_text(size = 7, family = "Helvetica"),
axis.title.y= element_text(size = 7, family = "Helvetica"),
strip.text.x = element_text(size = 7, family = "Helvetica"),
strip.text.y = element_text(size = 7, family = "Helvetica"),
strip.placement = "outside")
print(g)
dev.off()
} else if (type == "class"){
cor_df <- cbind(te_exp[,c("LTR", "LINE", "SINE")], sgn_df)
cor_long <- tidyr::pivot_longer(cor_df, cols = c("LTR", "LINE", "SINE"))
colnames(cor_long)[(length(colnames(cor_long))-1):length(colnames(cor_long))] <- c("TE name", "TE expression")
cor_longer <- tidyr::pivot_longer(cor_long, cols = colnames(cor_df)[4:length(colnames(cor_df))])
colnames(cor_longer)[(length(colnames(cor_longer))-1):length(colnames(cor_longer))] <- c("Signature name", "Score")
cor_longer$Cohort <- cohort
ggplot(data = cor_longer, aes(x = Score, y = `TE expression`)) +
geom_point() +
facet_grid(facets = `TE name` ~ `Signature name`, scales = "free") +
theme_minimal() +
scale_x_continuous(n.breaks = 3) +
theme(strip.placement = "outside")
} else if (type == "family"){
cor_df <- cbind(te_exp[,c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK")], sgn_df)
cor_long <- tidyr::pivot_longer(cor_df, cols = c("L1", "Alu", "ERV1", "ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK"))
colnames(cor_long)[(length(colnames(cor_long))-1):length(colnames(cor_long))] <- c("TE name", "TE expression")
cor_longer <- tidyr::pivot_longer(cor_long, cols = colnames(cor_df)[9:length(colnames(cor_df))])
colnames(cor_longer)[(length(colnames(cor_longer))-1):length(colnames(cor_longer))] <- c("Signature name", "Score")
cor_longer$Cohort <- cohort
ggplot(data = cor_longer, aes(x = Score, y = `TE expression`)) +
geom_point() +
facet_grid(facets = `TE name` ~ `Signature name`, scales = "free") +
theme_minimal() +
scale_x_continuous(n.breaks = 3) +
theme(strip.placement = "outside")
} else {
print("Wrong type! Available values: 'all', 'class' , 'family'.")
}
}
heatmap.all <- function(col_bin = T, cohorts_list = c("GSE56045", "GSE48556", "GSE58137"), selection = "all"){
chrt_df <- data.frame()
for (cohort in cohorts_list){
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sgn_df <- dplyr::bind_rows(sign_scores)
sgn_df <- t(sgn_df)
colnames(sgn_df) <- sgn_df[1,]
sgn_df <- sgn_df[-1,]
sgn_df <- dplyr::mutate_all(as.data.frame(sgn_df), function(x)as.numeric(x))
colnames(sgn_df) <- gsub(" ", "\n", colnames(sgn_df))
cor_df <- cbind(te_exp[,c("Age", "LTR", "LINE", "SINE", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR")], sgn_df)
cor_df <- as.data.frame(scale(cor_df))
cor_df <- dplyr::mutate_all(as.data.frame(cor_df), function(x)as.numeric(x))
cor_df$Cohort <- cohort
chrt_df <- rbind(chrt_df, cor_df)
}
chrt_df$Cohort <- plyr::mapvalues(chrt_df$Cohort,
from = c("E_TABM_1036", "GSE20142", "GSE47729",
"GSE48152", "GSE48556", "GSE56045",
"GSE58137"),
to = c("DILGOM", "FEHRMANN", "HVH",
"InCHIANTI", "GARP", "MESA",
"GTP"))
anno_colors <- list(Type = c("Class" = "#9B23E8", "Family" = "#F7730C", "Age" = "#0557E5", "Signature" = "#08FC74"))
cohort_colors <- list(Cohort = c("MESA" = "#E69AAB", "GTP" = "#82CBE6", "GARP" = "#E6DE6C"))
if (selection == "all"){
# Define TE types for annotation
anno <- data.frame(Feature = c("LTR", "LINE", "SINE", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR", "Age",
colnames(sgn_df)),
Type = c(rep("Class", 3), rep("Family", 8), "Age",
rep("Signature", length(colnames(sgn_df)))))
alt_anno <- data.frame(Type = t(anno$Type)[order(match(anno$Feature,colnames(chrt_df)[-length(colnames(chrt_df))]))])
rownames(alt_anno) <- colnames(chrt_df)[-length(colnames(chrt_df))]
ComplexHeatmap::Heatmap(t(chrt_df[,-length(colnames(chrt_df))]),
show_column_names = F,
show_column_dend = F,
show_row_dend = T,
cluster_rows = T,
cluster_columns = col_bin,
row_split = alt_anno$Type,
column_split = chrt_df$Cohort,
row_title = NULL,
column_title = NULL,
show_heatmap_legend = F,
bottom_annotation = HeatmapAnnotation(`Cohort` = chrt_df$Cohort, col = cohort_colors, show_annotation_name = F),
right_annotation = rowAnnotation(`Type` = alt_anno$Type, col = anno_colors, show_annotation_name = F)
)
} else if (selection == "family"){
# Define TE types for annotation
anno <- data.frame(Feature = c("Age", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR",
colnames(sgn_df)),
Type = c("Age", rep("Family", 8),
rep("Signature", length(colnames(sgn_df)))))
chrt_df <- chrt_df %>%
dplyr::select(c("Age", "L1", "L2", "ERV1", "ERVL",
"ERVL-MaLR","ERVK", "Alu", "MIR", colnames(sgn_df), "Cohort"))
alt_anno <- data.frame(Type = t(anno$Type)[order(match(anno$Feature,colnames(chrt_df)[-length(colnames(chrt_df))]))])
rownames(alt_anno) <- colnames(chrt_df)[-length(colnames(chrt_df))]
pdf(file = paste0("../Figures/images/", cohort,"_heatmap_family.pdf"),
width = 130*0.0394,
height = 110*0.0394)
ht <- ComplexHeatmap::Heatmap(t(chrt_df[,-length(colnames(chrt_df))]),
show_column_names = F,
show_column_dend = F,
show_row_dend = T,
cluster_rows = T,
cluster_columns = col_bin,
row_split = alt_anno$Type,
column_split = chrt_df$Cohort,
row_names_gp = grid::gpar(fontsize = 7, fontfamily = "Helvetica"),
row_title = NULL,
column_title = NULL,
show_heatmap_legend = F,
right_annotation = rowAnnotation(`Type` = alt_anno$Type,
col = anno_colors,
show_annotation_name = F)
)
draw(ht, show_heatmap_legend = F)
dev.off()
} else if (selection == "class"){
# Define TE types for annotation
anno <- data.frame(Feature = c("LTR", "LINE", "SINE", "Age",
colnames(sgn_df)),
Type = c(rep("Class", 3), "Age",
rep("Signature", length(colnames(sgn_df)))))
chrt_df <- chrt_df %>%
dplyr::select(c("LTR", "LINE", "SINE", "Age",
colnames(sgn_df), "Cohort"))
alt_anno <- data.frame(Type = t(anno$Type)[order(match(anno$Feature,colnames(chrt_df)[-length(colnames(chrt_df))]))])
rownames(alt_anno) <- colnames(chrt_df)[-length(colnames(chrt_df))]
ComplexHeatmap::Heatmap(t(chrt_df[,-length(colnames(chrt_df))]),
show_column_names = F,
show_column_dend = F,
show_row_dend = T,
cluster_rows = T,
cluster_columns = col_bin,
row_split = alt_anno$Type,
column_split = chrt_df$Cohort,
row_title = NULL,
column_title = NULL,
show_heatmap_legend = F,
bottom_annotation = HeatmapAnnotation(`Cohort` = chrt_df$Cohort, col = cohort_colors, show_annotation_name = F),
right_annotation = rowAnnotation(`Type` = alt_anno$Type, col = anno_colors, show_annotation_name = F)
)
}
}
quartile.all <- function(cohort = "GSE56045", te_class){
chrt_df <- data.frame()
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
# Read signature scores
sign_scores <- read_excel_allsheets(paste0("../Results/", cohort, "_230607.xlsx"))
# Define signature scores
sgn_df <- dplyr::bind_rows(sign_scores)
sgn_df <- t(sgn_df)
colnames(sgn_df) <- sgn_df[1,]
sgn_df <- sgn_df[-1,]
sgn_df <- dplyr::mutate_all(as.data.frame(sgn_df), function(x)as.numeric(x))
colnames(sgn_df) <- gsub(" ", "\n", colnames(sgn_df))
cor_df <- cbind(te_exp[,c("Age", "LTR", "LINE", "SINE","L1", "Alu", "ERV1",
"ERVL", "L2", "ERVL-MaLR", "MIR", "ERVK")], sgn_df)
cor_df <- as.data.frame(scale(cor_df))
cor_df <- dplyr::mutate_all(as.data.frame(cor_df), function(x)as.numeric(x))
cor_df$Cohort <- cohort
chrt_df <- rbind(chrt_df, cor_df)
chrt_df$Cohort <- plyr::mapvalues(chrt_df$Cohort,
from = c("E_TABM_1036", "GSE20142", "GSE47729",
"GSE48152", "GSE48556", "GSE56045",
"GSE58137"),
to = c("DILGOM", "FEHRMANN", "HVH",
"InCHIANTI", "GARP", "MESA",
"GTP"))
chrt_df <- chrt_df %>%
mutate(quartile = ntile(get(te_class), n = 4))
chrt_df$quartile <- as.factor(chrt_df$quartile)
chrt_q <- chrt_df %>%
dplyr::select(c("SASP", "Inflammatory\nchemokines", "Inflammatory\ncytokines",
"Senescence", "Inflammaging", "quartile")) %>%
tidyr::pivot_longer(cols = c("SASP", "Inflammatory\nchemokines",
"Inflammatory\ncytokines","Senescence", "Inflammaging"))
chrt_q$name <- factor(chrt_q$name, levels = c("SASP", "Inflammatory\nchemokines",
"Inflammatory\ncytokines", "Senescence", "Inflammaging"))
chrt_q$quartile <- plyr::mapvalues(chrt_q$quartile, from = c(1,2,3,4), to = c("Low", "Med", "Med", "High"))
# Calculate comparisons
comparisons <- compare_means(formula = value ~ quartile, data = chrt_q, group.by = "name") %>%
filter(p.signif != "ns")
comparisons$p.signif <- ifelse(comparisons$p <= 0.05 & comparisons$p > 0.01, "*",
ifelse(comparisons$p <= 0.01 & comparisons$p > 0.001, "**",
ifelse(comparisons$p <= 0.001, "***", "ns")))
pdf(file = paste0("../Figures/images/", cohort, "_quartile_", te_class, ".pdf"),
width = 165*0.0394,
height = 35*0.0394)
g <- ggplot(data = chrt_q, aes(x = quartile, y = value, group = quartile)) +
geom_boxplot(aes(fill = quartile), size = 0.2, outlier.shape = NA) +
scale_fill_manual(values = c("#0DC35A", "#FFE100", "#FF4300")) +
facet_grid(facets = ~ name) +
stat_pvalue_manual(comparisons, label = "p.signif", y.position = 3,
step.increase = 0.08, step.group.by = "name",
label.size = 2.5, bracket.size = 0.2) +
theme_pubr() +
theme(axis.text.x = element_text(size = 7, family = "Helvetica"),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y= element_blank(),
strip.text.x = element_blank(),
strip.text.y = element_blank(),
legend.position = "none") +
scale_x_discrete(labels = c("Low", "Med", "High")) +
scale_y_continuous(n.breaks = 3, breaks = c(-2,0,2)) +
xlab("Quartile") +
ylab("Score") +
theme(panel.spacing = unit(1, "cm"))
print(g)
dev.off()
}
##### Convert gene sets to GeneSetCollection objects
# Read gene sets
gsva_gene_sets <- read_excel_allsheets("../../chemo_pred/Data/gsva_gene_signatures_newIR.xlsx")
gsva_gs <- sapply(names(gsva_gene_sets),
FUN = function(y){
GSEABase::GeneSetCollection(sapply(names(gsva_gene_sets[[y]]),
FUN = function(x){
GSEABase::GeneSet(unique(mapIds(org.Hs.eg.db,
unique(gsva_gene_sets[[y]][x][!is.na(gsva_gene_sets[[y]][x])]),
column = "ENTREZID", keytype = "SYMBOL")),
setName = x, geneIdType = EntrezIdentifier())}, USE.NAMES = T)
)},
USE.NAMES = T)
##### GSVA Heatmap function
gsva.heatmap <- function(cohorts_list, te_list){
chrt_df <- data.frame()
for (cohort in cohorts_list){
# Read Gene expressions
gene_exp <- readRDS(paste0("../Data/Gene_Expression/", cohort, "_avg.rds"))
rownames(gene_exp) <- mapIds(org.Hs.eg.db, rownames(gene_exp), column = "ENTREZID", keytype = "SYMBOL")
# Read TE expressions
te_exp <- readRDS(paste0("../Data/TE_Expression/", cohort, "_TEs_pData.rds"))
rownames(te_exp) <- gsub(" 1", "", rownames(te_exp))
for (te in te_list){
te_exp <- te_exp %>%
mutate(quartile = ntile(get(te), n = 4))
te_exp$quartile <- as.factor(te_exp$quartile)
gene_exprs <- ExpressionSet(as.matrix(gene_exp),
phenoData = AnnotatedDataFrame(data.frame(quartile = t(te_exp)["quartile",])))
gsva_res <- sapply(gsva_gs, FUN = function(x){GSVA::gsva(gene_exprs, x)}, USE.NAMES = T)
mod <- model.matrix(~ factor(gene_exprs$quartile))
colnames(mod) <- c("q1", "q2", "q3", "q4")
tt_df <- data.frame()
for (gs_name in names(gsva_res)){
fit <- lmFit(gsva_res[[gs_name]], mod)
fit <- eBayes(fit)
res <- decideTests(fit, p.value=0.05)
contr <- makeContrasts(q1 - q4, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
tt <- topTable(tmp, sort.by = "P", n = Inf)
tt$diffexp <- "NO"
tt$diffexp[tt$logFC < -0.1 & tt$adj.P.Val < 0.05] <- "DOWN"
tt$diffexp[tt$logFC > 0.1 & tt$adj.P.Val < 0.05] <- "UP"
tt$diffexp <- factor(tt$diffexp, levels = c("DOWN", "NO", "UP"))
tt$pathway <- rownames(tt)
tt$gene_set <- gs_name
if (length(names(gsva_gene_sets[[gs_name]])) == 1){
rownames(tt)[tt$gene_set == gs_name] <- gs_name
tt$pathway[tt$gene_set == gs_name] <- gs_name
}
tt_df <- rbind(tt_df, tt)
}
tt_df$diffexp_factor <- plyr::mapvalues(tt_df$diffexp,
from = c("DOWN", "NO", "UP"),
to = c(-1,0,1))
tt_df$cohort <- cohort
tt_df$cohort <- plyr::mapvalues(tt_df$cohort,
from = c("E_TABM_1036", "GSE20142", "GSE47729",
"GSE48152", "GSE48556", "GSE56045",
"GSE58137"),
to = c("DILGOM", "FEHRMANN", "HVH",
"InCHIANTI", "GARP", "MESA",
"GTP"))
tt_df$TE <- te
chrt_df <- rbind(chrt_df, tt_df)
}
}
# save(chrt_df, file = "../Data/gsva_results_all_cohorts_newIR.Rda")
# load("../Data/gsva_results_all_cohorts.Rda")
chrt_wide <- tidyr::pivot_wider(chrt_df[,c("gene_set", "pathway", "diffexp_factor", "cohort", "TE")],
names_from = "TE", id_cols = c("cohort", "pathway", "gene_set"),
values_from = "diffexp_factor")
# Remove possible duplicates when shortened
chrt_wide <- chrt_wide[!grepl("GOBP_POSITIVE_REGULATION_OF_ACUTE_INFLAMMATORY_RESPONSE", chrt_wide$pathway), ]
chrt_wide <- chrt_wide[!grepl("GOBP_POSITIVE_REGULATION_OF_CYTOKINE_PRODUCTION_INVOLVED_IN_INFLAMMATORY_RESPONSE", chrt_wide$pathway), ]
chrt_wide <- chrt_wide[!grepl("GOBP_POSITIVE_REGULATION_OF_ACUTE_INFLAMMATORY_RESPONSE_TO_ANTIGENIC_STIMULUS ", chrt_wide$pathway), ]
# chrt_wide$pathway <- stringr::str_trunc(chrt_wide$pathway, width = 35, side = "center")
start_count <- 20
word_count <- 45
end_count <- word_count - (start_count + 4)
chrt_wide$pathway <- sapply(chrt_wide$pathway, FUN = function(x){ifelse(nchar(x) > word_count,
paste0(substring(x, 1, start_count), "...", substring(x, nchar(x)-end_count, nchar(x))),
x)})
colFn <- c("-1" = "dodgerblue3", "0" = "lightgray", "1" = "firebrick")
chrt_wide$gene_set <- plyr::mapvalues(chrt_wide$gene_set,
from = c("senescence", "inflammaging",
"dna_repair", "inflammatory_response",
"interferon"),
to = c("Senescence", "Inflammaging",
"DNA Repair", "Inflammatory Response",
"Interferon"))
gs_colors <- list(`Gene Set` = c("Senescence" = "#FF8B82", "Inflammaging" = "#9771DE",
"DNA Repair" = "#8AEDF5", "Interferon" = "#A2E07E",
"Inflammatory Response" = "#FADB93"))
# Drop DN, Inflammaging and Interferon
chrt_wide <- chrt_wide[chrt_wide$gene_set != "Inflammaging",]
chrt_wide <- chrt_wide[chrt_wide$gene_set != "Interferon",]
chrt_wide <- chrt_wide[chrt_wide$gene_set != "Senescence",]
chrt_wide <- chrt_wide[!(grepl(pattern = "_DN", chrt_wide$pathway)),]
chrt_wide <- chrt_wide[!(grepl(pattern = "_NEGATIVE_", chrt_wide$pathway)),]
chrt_wide <- chrt_wide[!(grepl(pattern = "COVID", chrt_wide$pathway)),]
# Remove duplicates
chrt_wide <- chrt_wide[!grepl(".*\\.{3}51", chrt_wide$pathway), ]
chrt_wide <- chrt_wide[!grepl(".*\\.{3}53", chrt_wide$pathway), ]
chrt_wide <- chrt_wide[!grepl(".*\\.{3}45", chrt_wide$pathway), ]
chrt_wide <- chrt_wide[!grepl(".*\\.{3}52", chrt_wide$pathway), ]
chrt_wide$pathway <- gsub("\\.*\\d*$", "", chrt_wide$pathway)
ht_list <- NULL
for (te in te_list){
chrt_plot <- cbind(MESA = chrt_wide[chrt_wide$cohort == "MESA",te],
GTP = chrt_wide[chrt_wide$cohort == "GTP",te],
GARP = chrt_wide[chrt_wide$cohort == "GARP",te])
rownames(chrt_plot) <- chrt_wide$pathway[chrt_wide$cohort == "MESA"]
colnames(chrt_plot) <- c("MESA", "GTP", "GARP")
if (te == "LINE"){
h <- rowAnnotation(`Pathway` = anno_text(rownames(chrt_plot),
gp = gpar(fontsize = 5,
fontfamily = "Helvetica"),
just = "left")) +
rowAnnotation(`Gene Set` = chrt_wide$gene_set[chrt_wide$cohort == "MESA"],
show_annotation_name = F, show_legend = F, col = gs_colors) +
ComplexHeatmap::Heatmap(chrt_plot, col = colFn,
show_heatmap_legend = F,
row_names_gp = gpar(fontsize = 5, fontfamily = "Helvetica"),
row_names_side = "left",
column_names_gp = gpar(fontsize = 5, fontfamily = "Helvetica"),
column_names_side = "top",