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Figure5_2_human.Rmd
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Figure5_2_human.Rmd
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---
title: "Human transcription factors"
author: "Lori D Kregar, Ahrim Youn, ..., Duygu Ucar"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
You essentially just need use the files in the output/F5/human/ folder.
Filtered means that you first filter TFs to those which had a significant p-value, and then average them to produce the heatmaps.
```{r setup, include=FALSE}
#knitr::opts_chunk$set(cache=TRUE)
#knitr::opts_chunk$set(autodep = TRUE)knitr::opts_chunk$set(cache=TRUE)
knitr::opts_chunk$set(autodep = TRUE)
opts_knit$set(root.dir = "/Users/karako/Dropbox (JAX)/MouseAging_clean/") #set root dir!
```
# Prepare the environment
```{r libraries, include=TRUE, echo=TRUE}
library(ComplexHeatmap, quietly = TRUE)
library(circlize, quietly = TRUE)
library(writexl, quietly = TRUE)
library(JASPAR2018, quietly = TRUE)
library(TFBSTools, quietly = TRUE)
library(dplyr, quietly = TRUE)
```
```{r hardcoded_environment, include=TRUE, echo=TRUE, cache=FALSE}
plots_in_pdf <- produce_plots_in_pdf <- TRUE
cor.cutoff=0.9
use_PFM_similarity_for_grouping <- TRUE
change_me_to_force_sourcing_chunk <- 10
source("code/TF_pfm_extension_functions.R")
cat("sourced\n")
```
```{r preprocess_data, include=TRUE, echo=TRUE, results='hide'}
# this function does the actual data processing, need not run it if you just need some plots after
source("code/heatmap_risk_human_ldk.R")
```
# Opening transcription factors in humans
```{r load_data, include=TRUE, echo=FALSE}
opening_closing_human <- vector("list", 2)
clusters_for_opening_closing <- c(7, 30)
idx <- 1
for(i in c(TRUE, FALSE)){
load(paste0("data/human_aging_footprint/combined_human_FC_p_CA_ha",i,".Rdata"))
# combine dataframe
df_FC <- add_colnames_for_type(combined_FC, "FC ")
df_pvals <- add_colnames_for_type(combined_p_vals, "log p value ")
df_both <- base::merge(as.data.frame(df_FC),
as.data.frame(df_pvals),
by="row.names",
all = TRUE)
df_both <- df_both[,c( "Row.names",
"FC F O",
"log p value F O",
"FC F Y",
"log p value F Y",
"FC M O",
"log p value M O",
"FC M Y",
"log p value M Y")]
colnames(df_both)[1] <- "Transcription factor"
df_both[,1] <- sapply(df_both[,1], function(x){
return(replaced_dot_by_comma <- gsub("\\.", ", ", x))
})
load(paste0("data/human_aging_footprint/raw_FC_p", i, ".Rdata"))
df_FC_raw <- add_colnames_for_type(raw_names_FC, "FC ")
df_pvals_raw <- add_colnames_for_type(raw_names_pvals, "log p value ")
df_both_raw <- merge(as.data.frame(df_FC_raw),
as.data.frame(df_pvals_raw),
by="row.names",
all = TRUE)
df_both_raw <- df_both_raw[,c( "Row.names",
"FC F O",
"log p value F O",
"FC F Y",
"log p value F Y",
"FC M O",
"log p value M O",
"FC M Y",
"log p value M Y")]
colnames(df_both_raw)[1] <- "Transcription factor"
# replace concatenated jaspar names by nicer ones
df_both_raw_names <- split_jaspar_name_return_df_of_names(df_both_raw$`Transcription factor`)
df_both_raw <- merge(df_both_raw_names, df_both_raw, by = "Transcription factor")
# merge raw by TF similarity
raw_matrix <- subset(df_both_raw, select = -c(`Transcription factor`,
`Jaspar_matrix_ID`, `name`))
rownames(raw_matrix) <- df_both_raw$`Transcription factor`
df_PFM_merged <- cluster_heatmaps_on_TF_similarity(raw_matrix)
as_df_PFM_merged <- as.data.frame(df_PFM_merged)
as_df_PFM_merged$`Transcription factor` <- rownames(as_df_PFM_merged)
as_df_PFM_merged <- as_df_PFM_merged[,c(ncol(as_df_PFM_merged),
1:(ncol(as_df_PFM_merged)-1))]
PBMC <- list('PBMC TF raw' = df_both_raw,
'PBMC TF (PFM)' = as_df_PFM_merged,
'PBMC TF (corr)' = df_both)
opening_closing_human[[idx]] <- PBMC
PBMC[[2]] <- PBMC[[3]] <- NULL
typeOfPeak <- ifelse(i == TRUE, "opening", "closing")
file_name <- paste0(typeOfPeak, "_transcription_factors_FC_p_human.xlsx")
write_xlsx(PBMC, path = paste0("output/F5/human/", file_name))
idx <- idx + 1
}
names(opening_closing_human) <- c("opening", "closing")
save(opening_closing_human,
file = "data/human_aging_footprint/human_TF_FC_p_xslx.Rdata")
```
```{r heatmap_plotting_function, include=TRUE, echo=TRUE}
plot_HM <- function(df_of_combined_per_type,
by_age = TRUE,
isSuperLong = FALSE,
mini = 10,
only_old = FALSE,
only_young = FALSE,
opening_,
filtered = FALSE){
# supply matrix of transcription factors with only FC or p values
if(filtered == FALSE){
old_cols <- c(3:4)
young_cols <- c(1:2)
}else{
old_cols <- young_cols <- c(1:2)
}
type_of_data <- ifelse(any(grepl("FC", colnames(df_of_combined_per_type))),
"Fold change", "log p value")
if(type_of_data == "Fold change"){
if(opening_){
colvec = colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#ffe6ed", "#c70039") # light and dark red for opening FC
)
}else{
colvec = colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#F4FAFE", "#273871") # light and dark blue for closing FC
)
}
}else{
if(opening_){
colvec=colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#fff1e6", "#e66700") # light and dark orange for opening p
#c("#FCF5F2", "#6D0026") # light and dark dark red
)
}else{
colvec=colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#e5f0db", "#587d36") #light and dark green for closing p
# c("#fff1e6", "#e66700") # light and dark orange for opening p
# c("#FCF5F2", "#6D0026") # light and dark dark red
)
}
}
input_df <- df_of_combined_per_type
rownames(input_df) <- input_df[,1]
input_df <- input_df[,-1]
cn <- colnames(input_df)
# change names and order depending if column ordered by gender or age
if(by_age){
# reorder columns
input_df <- input_df[,c(grep("Y$", cn), grep("O$", cn))]
colnames(input_df) <- ifelse(grepl(" F ", colnames(input_df)), "Female", "Male")
}else{
# rev so that young is on left, old on right
input_df <- input_df[,c(rev(grep(" F ", cn)), rev(grep(" M ", cn)))]
colnames(input_df) <- ifelse(grepl(" Y$", colnames(input_df)), "Young", "Old")
}
legend_plot <- Legend(col_fun = colvec,
title = type_of_data,
legend_height = unit(4, "cm"),
direction = ifelse(!opening_, "horizontal", "vertical"),
title_position = ifelse(!opening_, "topcenter", "topleft"))
legend_x <- ifelse(opening_, 0.965, 0.8)
legend_y <- ifelse(opening_, 0.05, 0.02)
if(by_age){
col_titles <- c("Young", "Old")
}else{
col_titles <- c("Female", "Male")
}
input_df <- as.matrix(input_df)
if(isSuperLong){
pad <- c(2, 2, 2, 20)
}else{
pad <- rep(2,4)
}
if(only_old == FALSE & only_young == FALSE){
draw(Heatmap(input_df[,1:2],
cluster_columns=F,
cluster_rows=F,
column_title=col_titles[1],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini)) +
Heatmap(input_df[,3:4],
cluster_columns=F,
cluster_rows=F,
column_title=col_titles[2],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini),
col=colvec),
padding = unit(pad, "mm"))
}else if(only_old){
draw(Heatmap(input_df[,old_cols],
cluster_columns=F,
cluster_rows=F,
column_title="Old",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini),
col=colvec),
padding = unit(pad, "mm"))
}else if(only_young){
draw(Heatmap(input_df[,young_cols],
cluster_columns=F,
cluster_rows=F,
column_title="Young",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini)),
padding = unit(pad, "mm"))
}
draw(legend_plot, just = c("right", "bottom"),
x = unit(legend_x, "npc"), y = unit(legend_y, "npc"))
}
plot_hm_side_by_side <- function(df_of_combined_per_type, df_other,
by_age = TRUE,
isSuperLong = FALSE,
mini = 10,
only_old = FALSE,
only_young = FALSE,
opening_,
filtered = FALSE){
#df_of_combined_per_type <- list_for_xls[[1]]
#df_other <- list_for_xls[[2]]
# isSuperLong <- TRUE
# mini = 10
# only_old = FALSE
# only_young = TRUE
# opening = TRUE
# filtered = TRUE
# supply matrix of transcription factors with only FC or p values
if(filtered == FALSE){
old_cols <- c(3:4)
young_cols <- c(1:2)
}else{
old_cols <- young_cols <- c(1:2)
}
type_of_data <- ifelse(any(grepl("FC", colnames(df_of_combined_per_type))),
"Fold change", "log p value")
if(type_of_data == "Fold change"){
if(opening_){
colvec = colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#ffe6ed", "#c70039") # light and dark red for opening FC
)
}else{
colvec = colorRamp2(c(quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.05),
quantile(unlist(df_of_combined_per_type[,c(2:ncol(df_of_combined_per_type))]),0.95)),
c("#F4FAFE", "#273871") # light and dark blue for closing FC
)
}
}
#else{
if(opening_){
colvec_p=colorRamp2(c(quantile(unlist(df_other[,c(2:ncol(df_other))]),0.05),
quantile(unlist(df_other[,c(2:ncol(df_other))]),0.95)),
c("#fff1e6", "#e66700") # light and dark orange for opening p
#c("#FCF5F2", "#6D0026") # light and dark dark red
)
}else{
colvec_p=colorRamp2(c(quantile(unlist(df_other[,c(2:ncol(df_other))]),0.05),
quantile(unlist(df_other[,c(2:ncol(df_other))]),0.95)),
c("#e5f0db", "#587d36") #light and dark green for closing p
# c("#fff1e6", "#e66700") # light and dark orange for opening p
# c("#FCF5F2", "#6D0026") # light and dark dark red
)
}
#}
input_df <- df_of_combined_per_type
rownames(input_df) <- input_df[,1]
input_df <- input_df[,-1]
input_df_other <- df_other
rownames(input_df_other) <- input_df_other[,1]
input_df_other <- input_df_other[,-1]
cn <- colnames(input_df)
cn_p <- colnames(input_df_other)
# change names and order depending if column ordered by gender or age
if(by_age){
# reorder columns
input_df <- input_df[,c(grep("Y$", cn), grep("O$", cn))]
colnames(input_df) <- ifelse(grepl(" F ", colnames(input_df)), "Female", "Male")
input_df_other <- input_df_other[,c(grep("Y$", cn_p), grep("O$", cn_p))]
colnames(input_df_other) <- ifelse(grepl(" F ", colnames(input_df_other)), "Female", "Male")
}else{
# rev so that young is on left, old on right
input_df <- input_df[,c(rev(grep(" F ", cn)), rev(grep(" M ", cn)))]
colnames(input_df) <- ifelse(grepl(" Y$", colnames(input_df)), "Young", "Old")
input_df_other <- input_df_other[,c(rev(grep(" F ", cn_p)), rev(grep(" M ", cn_p)))]
colnames(input_df_other) <- ifelse(grepl(" Y$", colnames(input_df_other)), "Young", "Old")
}
legend_plot <- Legend(col_fun = colvec,
title = type_of_data,
legend_height = unit(ifelse(opening_, 4, 6), "cm"),
direction = ifelse(!opening_, "horizontal", "vertical"),
title_position = ifelse(!opening_, "lefttop", "topcenter"))
legend_x <- ifelse(opening_, 0.95, 0.95)
legend_y <- ifelse(opening_, 0.35, 0.06)
Legend_p <- Legend(col_fun = colvec_p,
title = "Log p value",
legend_height = unit(ifelse(opening_, 4, 6), "cm"),
direction = ifelse(!opening_, "horizontal", "vertical"),
title_position = ifelse(!opening_, "lefttop", "topcenter"))
legend_x_p <- ifelse(opening_, 0.95, 0.95)
legend_y_p <- ifelse(opening_, 0.05, 0.015)
pd = packLegend(legend_plot, Legend_p)
if(by_age){
col_titles <- c("Young", "Old")
}else{
col_titles <- c("Female", "Male")
}
input_df <- as.matrix(input_df)
input_df_other <- as.matrix(input_df_other)
if(isSuperLong){
pad <- c(2, 2, 2, 20)
}else{
pad <- rep(2,4)
}
if(only_old == FALSE & only_young == FALSE){
draw(Heatmap(input_df[,1:2],
cluster_columns=F,
cluster_rows=F,
column_title=col_titles[1],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini)) +
Heatmap(input_df[,3:4],
cluster_columns=F,
cluster_rows=F,
column_title=col_titles[2],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini),
col=colvec),
padding = unit(pad, "mm"))
}else if(only_old){
draw(Heatmap(input_df[,old_cols],
cluster_columns=F,
cluster_rows=F,
#column_title="Old",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini),
col=colvec) +
Heatmap(input_df_other[,old_cols],
cluster_columns=F,
cluster_rows=F,
#column_title="Old",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini),
col=colvec_p),
column_title = "Old",
padding = unit(pad, "mm"))
}else if(only_young){
draw(Heatmap(input_df[,young_cols],
cluster_columns=F,
cluster_rows=F,
#column_title="Young",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini)) +
Heatmap(input_df_other[,young_cols],
cluster_columns=F,
cluster_rows=F,
#column_title="Young",
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec_p,
show_heatmap_legend = FALSE,
# row_names_max_width = max_text_width(
# rownames(input_df),
# gp = gpar(fontsize = 10)),
row_names_gp = gpar(fontsize = mini)),
column_title = "Young",
padding = unit(pad, "mm"))
}
# draw(pd, just = c("right", "bottom"),
# x = unit(1, "cm"), y = unit(1, "cm"))
#x = unit(legend_x, "npc"), y = unit(legend_y, "npc"))
draw(legend_plot, just = c("right", "bottom"),
x = unit(legend_x, "npc"), y = unit(legend_y, "npc"))
draw(Legend_p, just = c("right", "bottom"),
x = unit(legend_x_p, "npc"), y = unit(legend_y_p, "npc"))
# draw_title(ifelse(only_young, "Young", "Old"),
# "column", 1)
}
```
# Opening peaks in human PBMC: Fold change
## Separated by age
```{r heatmap_opening_human, include=TRUE, echo = TRUE}
if(use_PFM_similarity_for_grouping){
opening_FC <- opening_closing_human[["opening"]][[2]]
opening_FC$`Transcription factor` <- sapply(opening_FC$`Transcription factor`,
introduce_new_line_to_rownames)
}else{
opening_FC <- opening_closing_human[["opening"]][[3]]
}
opening_FC <- opening_FC[,c(1, grep("FC", colnames(opening_FC)))]
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_FC_old_young_by_age_PFM.pdf")
}else{
pdf("output/F5/human_opening_FC_old_young_by_age_TF.pdf")
}
}
plot_HM(opening_FC, isSuperLong = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
## Separated by gender
```{r heatmap_opening_human_by_gender, include=TRUE, echo = TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_FC_old_young_by_gender_PFM.pdf")
}else{
pdf("output/F5/human_opening_FC_old_young_by_gender_TF.pdf")
}
}
plot_HM(opening_FC, by_age = FALSE, isSuperLong = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
## Only old
```{r human_FC_only_old, echo=TRUE, include=TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_FC_old_PFM.pdf",
width = 5, height = 5)
}else{
pdf("output/F5/human_opening_FC_old_TF.pdf")
}
}
opening_FC$`Transcription factor` <- gsub("\n ", "", opening_FC$`Transcription factor`)
opening_FC$`Transcription factor` <- sapply(opening_FC$`Transcription factor`,
introduce_new_line_to_rownames, new_line_after = 25)
plot_HM(opening_FC, isSuperLong = TRUE, only_old = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
## Only old and significant: Fold change: filtered before averaging
```{r opening_old_sig_FC, include=TRUE, echo=TRUE}
filter_threshold <- -log(0.05)
opening_old_filtered <- opening_closing_human[[1]][[1]]
opening_old_filtered_fc <- only_relevant_cols(opening_old_filtered, "FC")
opening_old_filtered_pv <- only_relevant_cols(opening_old_filtered, "p value")
filtered_opening_old_both <- keep_only_significant_FC_pv(opening_old_filtered_fc,
opening_old_filtered_pv,
" O",
filter_threshold)
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_FC_old_PFM_sig.pdf",
width = 5, height = 5)
}else{
pdf("output/F5/human_opening_FC_old_TF_sig.pdf")
}
}
FC_sig_old <- filtered_opening_old_both[[1]]
FC_sig_old_as_matrix <- FC_sig_old
rownames(FC_sig_old_as_matrix) <- FC_sig_old_as_matrix$`Transcription factor`
FC_sig_old_as_matrix[,1] <- NULL
FC_sig_old_as_matrix <- as.matrix(FC_sig_old_as_matrix)
open_sig_old_merged <- cluster_heatmaps_on_TF_similarity(FC_sig_old_as_matrix,
return_dend = TRUE)
FC_sig_old_pad_merged <- as.data.frame(open_sig_old_merged[[1]])
rownames(FC_sig_old_pad_merged) <- sapply(rownames(FC_sig_old_pad_merged),
introduce_new_line_to_rownames,
new_line_after = 30)
FC_sig_old_pad <- cbind(`Transcription factor` = rownames(FC_sig_old_pad_merged),
`FC F Y` = 0, `FC M Y` = 0, FC_sig_old_pad_merged[,1:2])
plot_HM(FC_sig_old_pad, isSuperLong = TRUE, only_old = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
## Only old and significant: log p value: filtered before averaging
```{r opening_old_sig_pv, include=TRUE, echo=TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_pv_old_PFM_sig.pdf",
width = 5, height = 5)
}else{
pdf("output/F5/human_opening_pv_old_TF_sig.pdf")
}
}
pv_sig_old <- filtered_opening_old_both[[2]]
pv_sig_old_pad <- cbind(`Transcription factor` = pv_sig_old[,1], `FC F Y` = 0, `FC M Y` = 0, pv_sig_old[,2:3])
plot_HM(pv_sig_old_pad, isSuperLong = TRUE, only_old = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
# Opening peaks in human PBMC: log p values
## Separated by age
```{r heatmap_opening_human_p, include=TRUE, echo=TRUE}
if(use_PFM_similarity_for_grouping){
opening_pv <- opening_closing_human[["opening"]][[2]]
opening_pv$`Transcription factor` <- sapply(opening_pv$`Transcription factor`,
introduce_new_line_to_rownames)
}else{
opening_pv <- opening_closing_human[["opening"]][[3]]
}
opening_pv <- opening_pv[,c(1, grep("log ", colnames(opening_pv)))]
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_pv_old_young_by_age_PFM.pdf")
}else{
pdf("output/F5/human_opening_pv_old_young_by_age_TF.pdf")
}
}
plot_HM(opening_pv, isSuperLong = TRUE, opening = TRUE)
if(plots_in_pdf){
dev.off()
}
```
```{r opening_only_old_p, include=TRUE, echo=TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_opening_pv_old_PFM.pdf",
width = 5, height = 5)
}else{
pdf("output/F5/human_opening_pv_old_TF.pdf")
}
}
opening_pv$`Transcription factor` <- gsub("\n ", "", opening_pv$`Transcription factor`)
opening_pv$`Transcription factor` <- sapply(opening_pv$`Transcription factor`,
introduce_new_line_to_rownames,
new_line_after = 25)
plot_HM(opening_pv, isSuperLong = TRUE, opening = TRUE, only_old = TRUE)
if(plots_in_pdf){
dev.off()
}
```
# Closing peaks in human PBMC: Fold change
# Young and old
```{r heatmap_closing_human, include=TRUE, echo=TRUE, out.width="100%", fig.height=15}
if(use_PFM_similarity_for_grouping){
closing_FC <- opening_closing_human[["closing"]][[2]]
closing_FC$`Transcription factor` <- sapply(closing_FC$`Transcription factor`,
introduce_new_line_to_rownames)
}else{
closing_FC <- opening_closing_human[["closing"]][[3]]
}
closing_FC <- closing_FC[,c(1, grep("FC", colnames(closing_FC)))]
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_closing_FC_old_young_PFM.pdf",
width = 10, height = 15)
}else{
pdf("output/F5/human_closing_FC_old_young_TF.pdf",
width = 10, height = 15)
}
}
plot_HM(closing_FC, isSuperLong = TRUE, mini = 7, opening = FALSE)
if(plots_in_pdf){
dev.off()
}
```
## Only young
```{r closing_human_young_only_FC, include=TRUE, echo=TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_closing_FC_young_PFM.pdf",
width = 5, height = 15)
}else{
pdf("output/F5/human_closing_FC_young_TF.pdf",
width = 6, height = 15)
}
}
plot_HM(closing_FC, isSuperLong = TRUE, mini = 7, opening = FALSE,
only_young = TRUE)
if(plots_in_pdf){
dev.off()
}
```
# Closing peaks in human PBMC: log p values
```{r heatmap_closing_p, include=TRUE, echo=TRUE, out.width="100%", fig.height=15}
if(use_PFM_similarity_for_grouping){
closing_pv <- opening_closing_human[["closing"]][[2]]
closing_pv$`Transcription factor` <- sapply(closing_pv$`Transcription factor`,
introduce_new_line_to_rownames)
}else{
closing_pv <- opening_closing_human[["closing"]][[3]]
}
closing_pv <- closing_pv[,c(1, grep("log ", colnames(closing_pv)))]
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_closing_pv_old_young_PFM.pdf",
width = 7, height = 15)
}else{
pdf("output/F5/human_closing_pv_old_young_TF.pdf",
width = 7, height = 15)
}
}
plot_HM(closing_pv, isSuperLong = TRUE, mini = 7, opening = FALSE)
if(plots_in_pdf){
dev.off()
}
```
## Only young
```{r only_young_closing_pv, include=TRUE, echo=TRUE}
if(plots_in_pdf){
if(use_PFM_similarity_for_grouping){
pdf("output/F5/human_closing_pv_young_PFM.pdf",
width = 5, height = 15)
}else{
pdf("output/F5/human_closing_pv_young_TF.pdf",
width = 5, height = 15)
}
}
plot_HM(closing_pv, isSuperLong = TRUE, mini = 7, opening = FALSE,
only_young = TRUE)
if(plots_in_pdf){
dev.off()
}
```
```{r filter_and_plot, include=TRUE, echo=TRUE}
thr <- -log(0.05)
oc <- c("opening", "closing")
fp <- c("df_FC", "df_pv")
sizes <- list(opening = c(4.5,6), closing = c(4.5, 8))
sizing_both <- list(opening = c(5.5,6), closing = c(5.5, 8))
significant_per_open_close <- vector("list", length = length(oc))
names(significant_per_open_close) <- oc
idx_i <- 0
for (i in oc) {
idx_i <- idx_i + 1
# filter columns from raw dataframes
fcs <- only_relevant_cols(opening_closing_human[[i]][[1]], "FC")
pvs <- only_relevant_cols(opening_closing_human[[i]][[1]], "p value")
filter_for <- ifelse(i == "opening", " O", " Y")
# keep only those rows which are significant in at least 1 old/young
new_fc_pv <- keep_only_significant_FC_pv(fcs, pvs, filter_by = filter_for,
threshold = thr)
significant_per_open_close[[i]] <- new_fc_pv
list_for_xls <- vector("list", length = length(fp))
idx_j <- 0
for(j in fp){
idx_j <- idx_j + 1
# merge by TF similarity
hm_matrix <- as.matrix(new_fc_pv[[j]][,2:3])
rownames(hm_matrix) <- new_fc_pv[[j]][,1]
fc_merged <- cluster_heatmaps_on_TF_similarity(hm_matrix,
return_dend = TRUE)
save(fc_merged, file = paste0("data/human_aging_footprint/human_tree_sig_filtered_merged_", i, "_", j, ".RData"))
merged_rows_format <- fc_merged[[1]]
rownames(merged_rows_format) <- sapply(rownames(merged_rows_format),
introduce_new_line_to_rownames,
new_line_after = ifelse(i == "opening", 20, 30))
# draw heatmaps
if(produce_plots_in_pdf){
pdf(paste0("output/F5/human/human_TF_filtered_merged_", i, "_", j, ".pdf"),
width = sizes[[i]][1], height = sizes[[i]][2])
}
merged_rows_format_df <- as.data.frame(merged_rows_format)
merged_rows_format_df <- cbind(`Transcription factor` = rownames(merged_rows_format_df),
merged_rows_format_df)
plot_HM(merged_rows_format_df,
isSuperLong = TRUE,
mini = 10, #ifelse(i == "closing", 10, 10),
only_old = ifelse(i == "opening", TRUE, FALSE),
only_young = ifelse(i == "closing", TRUE, FALSE),
opening = ifelse(i == "opening", TRUE, FALSE),
filtered = TRUE)
if(produce_plots_in_pdf){
dev.off()
}
list_for_xls[[idx_j]] <- merged_rows_format_df
}
# draw dendrogram
holder2 <- produce_dendrogram_plot(fc_merged[[3]],
num_clusters = length(unique(fc_merged[[2]])),
subtitle_ = paste("Human PBMC,", i),
path = paste0("output/F5/human/human_tree_filtered_merged_", i, ".pdf"),
very_tall = ifelse(i == "opening", 1, 2.4),
sub_offset = ifelse(i == "opening", 0.035, 0.015))
# produce spreadsheet
dfs_without_newline <- lapply(list_for_xls, function(x){
x[,1] <- gsub("\n", "", x[,1])
return(x)
})
names(dfs_without_newline) <- c("Fold change", "log p value (FDR)")
file_name_list <- paste0("human_", i, "_transcription_factors_FC_p_merged_filtered.xlsx")
write_xlsx(dfs_without_newline, path = paste0("output/F5/human/", file_name_list))
# draw heatmaps side by side
if(produce_plots_in_pdf){
pdf(paste0("output/F5/human/human_TF_filtered_merged_", i, "_both.pdf"),
width = sizing_both[[i]][1], height = sizing_both[[i]][2])
}
one <- list_for_xls[[1]]
the_other <- list_for_xls[[2]]
plot_hm_side_by_side(one, the_other,
isSuperLong = TRUE,
mini = 10, #ifelse(i == "closing", 10, 10),
only_old = ifelse(i == "opening", TRUE, FALSE),
only_young = ifelse(i == "closing", TRUE, FALSE),
opening = ifelse(i == "opening", TRUE, FALSE),