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Figure5_1_mouse.Rmd
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---
title: "Opening and closing transcription factors in mice"
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
---
# Prepare the environment
Note that some Rmd chunks are set to "eval=FALSE". This was to remove duplication of written output files.
However, if set to TRUE and produce_plots_in_pdf=FALSE, this Rmd file will generate plots in the html document.
```{r setup, include=FALSE}
#knitr::opts_chunk$set(cache=TRUE)
#knitr::opts_chunk$set(autodep = TRUE)
require(knitr)
opts_knit$set(root.dir = "/Users/karako/Dropbox (JAX)/MouseAging_clean/") #set root dir!
```
```{r libraries, echo=TRUE, include=FALSE}
library(ComplexHeatmap)
library(circlize)
library(graph)
library(RBGL)
library(Vennerable)
library(writexl)
library(plyr)
library(dplyr)
library(JASPAR2018)
library(rtracklayer)
library(TFBSTools)
library(dendextend)
library(RColorBrewer)
library(NbClust)
library(factoextra)
```
```{r hardcoded_options, include=TRUE, echo=TRUE}
cor.cutoff=0.9
use_PFM_similarity_for_grouping <- TRUE
produce_plots_in_pdf <- TRUE
hm_width_pdf <- 8
hm_height_pdf <- 8
filter_pvals <- TRUE
pq_value_cutoff <<- 0.05
source(file = "code/TF_pfm_extension_functions.R")
```
```{r prepare_data, include=TRUE, echo=TRUE, results='hide'}
# this function does most of the heavy lifting to take BiFET results and
# produce RData objects that contain the fold change and p-value matrices
source(file = "code/heatmap_risk_mouse_ldk.R")
```
```{r load_extra_functions, include=TRUE, echo=TRUE}
source("code/merge_TF_family_wf_helix.r")
load("data/mouse_aging_footprint/mouse_unexpressed_genelist.Rdata")
TFname<-function(TF){
if(length(TF)>0)
for(i in 1:length(TF)){
tmp=strsplit(TF[i],"")[[1]]
TF[i]=toupper(paste(tmp[-(1:7)],collapse=""))
}
return(TF)
}
```
```{r analyse_opening_closing, include=TRUE, echo=TRUE}
tissue=c("PBL","spleen","memory","naive")
opening=closing=vector("list",length(tissue))
names(opening)=names(closing)=tissue
for(i in 1:length(tissue)){
load(paste("data/mouse_aging_footprint/topregulator",tissue[i],"_ldk.Rdata",sep=""))
opening[[i]]= rownames(combinedheatmap[["increasing"]])
closing[[i]]= rownames(combinedheatmap[["decreasing"]])
}
# only keep opening and closing peaks that appear across all tissues
tmp=table(unlist(opening))
commonopening=names(tmp)[tmp==4]
tmp=table(unlist(closing))
commonclosing=names(tmp)[tmp==4]
for(i in 1:length(tissue)){
opening[[i]]=unique(TFname(opening[[i]]))
closing[[i]]=unique(TFname(closing[[i]]))
}
save(opening,closing,file="data/mouse_aging_footprint/TFlist.Rdata")
openingheatmap=NULL
closingheatmap=NULL
for(i in 1:length(tissue)){
load(paste("data/mouse_aging_footprint/topregulator",tissue[i],"_ldk.Rdata",sep=""))
openingheatmap=cbind(openingheatmap,combinedheatmap[["increasing"]][commonopening,])
}
starting_heatmap <- openingheatmap
colnames(starting_heatmap) <- paste(rep(tissue, each = 4), colnames(starting_heatmap))
# merge TFs that correlate based on their fold change
general_grouping <- merge.TF.family(starting_heatmap,TFfamily)
```
# Generate spreadsheets
```{r opening_peaks_spreadsheet_sanity, include=TRUE, echo=TRUE}
tissue <- c("PBL", "spleen", "memory", "naive")
pt <- c("opening", "closing") # pt: peak type
# check if top regulator the same as matall2[[4]]
for(ti in tissue){
load(paste0("data/mouse_aging_footprint/topregulator",ti,"_ldk.Rdata"))
increasing_hm_from_topreg <- combinedheatmap[["increasing"]]
decreasing_hm_from_topreg <- combinedheatmap[["decreasing"]]
load(paste0("data/mouse_aging_footprint/", ti, "_TRUE__matall_lists_ldk.Rdata"))
matall2_increasing <- matall2
increasing_identical <- all(increasing_hm_from_topreg == matall2_increasing[[4]])
load(paste0("data/mouse_aging_footprint/", ti, "_FALSE__matall_lists_ldk.Rdata"))
matall2_decreasing <- matall2
decreasing_identical <- all(decreasing_hm_from_topreg == matall2_decreasing[[4]])
if(increasing_identical & decreasing_identical){
print(paste(ti, "is fine"))
}else{
print(paste(ti, " IS NOT IDENTICAL!"))
}
}
# if TRUE --> use matall2[[4]] for fold change and matall2[[1]] for p-values
```
```{r full_peaks_spreadsheet, include=TRUE, echo=TRUE}
list_of_peaks_per_tissue <- vector("list", length = length(pt))
names(list_of_peaks_per_tissue) <- pt
for(typeOfPeak in pt){
# prepare a named list with dataframes for excel
list_of_df <- vector("list", length = length(tissue))
names(list_of_df) <- tissue
for(ti in tissue){
if(typeOfPeak == "opening"){
load(paste0("data/mouse_aging_footprint/", ti, "_TRUE__matall_lists_ldk.Rdata"))
}else{
load(paste0("data/mouse_aging_footprint/", ti, "_FALSE__matall_lists_ldk.Rdata"))
}
df_FC <- matall2[[4]]
colnames(df_FC) <- paste0("FC ", colnames(df_FC))
df_pvals <- matall2[[1]]
colnames(df_pvals) <- paste0("log p value ", colnames(df_pvals))
all(sort(rownames(df_FC)) == sort(rownames(df_pvals)))
df_FC_pval <- merge(as.data.frame(df_FC), as.data.frame(df_pvals),
by="row.names", all.x=TRUE)
colnames(df_FC_pval)[1] <- "Transcription factor"
df_FC_pval <- df_FC_pval[,c( "Transcription factor",
"FC B6 3mo",
"log p value B6 3mo",
"FC B6 18mo",
"log p value B6 18mo",
"FC NZO 3mo",
"log p value NZO 3mo",
"FC NZO 18mo",
"log p value NZO 18mo")]
df_FC_pval_names <- split_jaspar_name_return_df_of_names(df_FC_pval$`Transcription factor`)
df_FC_pval <- merge(df_FC_pval_names, df_FC_pval, by = "Transcription factor")
list_of_df[[ti]] <- df_FC_pval
}
names(list_of_df)[3:4] <- c("CD8 memory", "CD8 naive")
list_of_peaks_per_tissue[[typeOfPeak]] <- list_of_df
# write excel spreadsheet
file_name <- paste0(typeOfPeak, "_transcription_factors_FC_p_mouse.xlsx")
write_xlsx(list_of_df, path = paste0("output/F5/mouse/", file_name))
}
save(list_of_peaks_per_tissue, file = "data/mouse_aging_footprint/mouse_TF_FC_p_xlsx.Rdata")
```
# Opening peaks in old mice from both strains: Fold change
```{r plot_all_old_opening_homogeneous, include=TRUE, echo=TRUE, fig.height=9, fig.width=9, eval=TRUE}
if(use_PFM_similarity_for_grouping){
hm_merged <- cluster_heatmaps_on_TF_similarity(starting_heatmap)
}else{
newgroup_all_old = general_grouping #merge.TF.family(starting_heatmap,TFfamily)
hm_merged = merge.matrix(starting_heatmap, newgroup_all_old)
}
tmp_all_old=hm_merged[,grepl("18mo", colnames(hm_merged))]
rownames(tmp_all_old) <- toupper(rownames(tmp_all_old))
colvec_all_old=colorRamp2(c(quantile(tmp_all_old,0.05),
quantile(tmp_all_old,0.95)),
c("#ffe6ed", "#c70039") # light and dark red for opening
#c("#F4FAFE", "#273871")
)
lgd <- (Legend(col_fun = colvec_all_old,
title = "Fold change",
legend_height = unit(4, "cm")))
# if(produce_plots_in_pdf){
# if(use_PFM_similarity_for_grouping){
# pdf("output/F5/plot_mouse_footprints_opening_old_only_PFM.pdf",
# width = hm_width_pdf, height = hm_height_pdf)
# }else{
# pdf("output/F5/plot_mouse_footprints_opening_old_only_TF_fam.pdf",
# width = hm_width_pdf, height = hm_height_pdf)
# }
# }
tissue=c("PBL","spleen","CD8 memory","CD8 naive")
remove_mouse_tissue_from_colname <- function(x){
splitted <- strsplit(x, " ")[[1]]
species <- splitted[grepl("B6|NZO", splitted)]
age <- splitted[grepl("3mo|18mo", splitted)]
return(paste0(species, " ", age, collapse = ""))
}
# if any colname contains tissue name --> remove tissue name
if(any(grepl("spleen", colnames(tmp_all_old)))){
new_colnames <- sapply(colnames(tmp_all_old), remove_mouse_tissue_from_colname)
colnames(tmp_all_old) <- new_colnames
}
rownames(tmp_all_old) <- sapply(rownames(tmp_all_old),
introduce_new_line_to_rownames,
new_line_after = 30)
draw(Heatmap(tmp_all_old[,1:2],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[1],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec_all_old,
show_heatmap_legend = FALSE,
row_names_max_width = max_text_width(
rownames(tmp_all_old),
gp = gpar(fontsize = 10))) +
Heatmap(tmp_all_old[,3:4],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[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(tmp_all_old),
gp = gpar(fontsize = 10)),
col=colvec_all_old) +
Heatmap(tmp_all_old[,5:6],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[3],
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(tmp_all_old),
gp = gpar(fontsize = 10)),
col=colvec_all_old) +
Heatmap(tmp_all_old[,7:8],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[4],
column_title_gp = gpar(fontsize = 10),
#column_names_gp = gpar(fontsize = 7),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
row_names_max_width = max_text_width(
rownames(tmp_all_old),
gp = gpar(fontsize = 10)),
col=colvec_all_old),
padding = unit(c(2, 2, 2, 20), "mm"))
draw(lgd, just = c("right", "bottom"),
x = unit(0.965, "npc"), y = unit(0.05, "npc"))
# if(produce_plots_in_pdf){
# dev.off()
# }
```
```{r mouse_opening_dendrogram_of_merged, include=TRUE, echo=TRUE}
hm_merged_dend <- cluster_heatmaps_on_TF_similarity(starting_heatmap,
return_dend = TRUE)
plot_dend_basic <- produce_dendrogram_plot(some_dend = hm_merged_dend[[3]],
num_clusters = length(unique(hm_merged_dend[[2]])),
subtitle_ = "Organism: mouse, peak type: opening",
path = "output/F5/mouse/mouse_opening_dendrogram_bw.pdf")
plot_dend <- produce_dendrogram_plot(some_dend = hm_merged_dend[[3]],
num_clusters = length(unique(hm_merged_dend[[2]])),
subtitle_ = "Organism: mouse, peak type: opening",
path = "output/F5/mouse/mouse_opening_dendrogram_colour.pdf",
use_colours = TRUE,
dark_background = "#AFAFAF", box_border = 1)
```
# Opening peaks in old mice from both strains: log p-values
```{r heatmap_of_p_values, include=TRUE, echo=TRUE, fig.height=9, fig.width=9, eval=TRUE}
# produce dataframe
extract_pvals_opening_old <- lapply(list_of_peaks_per_tissue[["opening"]], function(x){
#x <- list_of_peaks_per_tissue[["opening"]][[1]]
only_p_val_cols <- x[,c(TRUE, grepl("p value", colnames(x)[2:ncol(x)]))]
only_old <- only_p_val_cols[,c(TRUE, grepl("18mo", colnames(only_p_val_cols)[2:ncol(only_p_val_cols)]))]
only_old_and_common <- only_old[which(only_old[,1] %in% commonopening),]
return(only_old_and_common)
})
# add tissue to colnames, which are "strain age"
for(i in seq_along(extract_pvals_opening_old)){
colnames(extract_pvals_opening_old[[i]])[2] <- paste0(names(extract_pvals_opening_old)[i], "_", colnames(extract_pvals_opening_old[[i]])[2])
colnames(extract_pvals_opening_old[[i]])[3] <- paste0(names(extract_pvals_opening_old)[i], "_", colnames(extract_pvals_opening_old[[i]])[3])
}
# combine all dataframes
df_heat_pvals <- join_all(extract_pvals_opening_old, by = "Transcription factor")
df_heat_pvals_to_mat <- df_heat_pvals
rownames(df_heat_pvals_to_mat) <- df_heat_pvals_to_mat[,1]
df_heat_pvals_to_mat <- df_heat_pvals_to_mat[,-c(1)]
# plot
matrix_p_values_all_old <- as.matrix(df_heat_pvals_to_mat)
colnames(matrix_p_values_all_old) <- sapply(colnames(matrix_p_values_all_old), function(x){
#x <- colnames(matrix_p_values_all_old)[1]
only_strain_age <- strsplit(x, " ")[[1]]
only_strain_age <- paste0(only_strain_age[c(length(only_strain_age)-1,length(only_strain_age))],
collapse = " ")
})
# here, ensure you use the FC grouping for p values, since grouping depends on
# FC correlation
if(use_PFM_similarity_for_grouping){
colnames(matrix_p_values_all_old) <- paste(rep(tissue, each = 2), colnames(matrix_p_values_all_old))
tmp_all_old_pvals <- cluster_heatmaps_on_TF_similarity(matrix_p_values_all_old)
}else{
heatmap_all_old_pvals <- merge.matrix(matrix_p_values_all_old, newgroup_all_old)
tmp_all_old_pvals <- heatmap_all_old_pvals
}
rownames(tmp_all_old_pvals) <- toupper(rownames(tmp_all_old_pvals))
rownames(tmp_all_old_pvals) <- sapply(rownames(tmp_all_old_pvals),
introduce_new_line_to_rownames,
new_line_after = 30)
if(any(grepl("spleen", colnames(tmp_all_old_pvals)))){
colnames_wo_tissue <- sapply(colnames(tmp_all_old_pvals),
remove_mouse_tissue_from_colname)
colnames(tmp_all_old_pvals) <- colnames_wo_tissue
}
colvec_all_old_pvals=colorRamp2(c(quantile(tmp_all_old_pvals,0.05),
quantile(tmp_all_old_pvals,0.95)),
#
c("#fff1e6", "#e66700"))
legend_p <- (Legend(col_fun = colvec_all_old_pvals,
title = "log p-value",
legend_height = unit(4, "cm")))
# if(produce_plots_in_pdf){
# if(use_PFM_similarity_for_grouping){
# pdf("output/F5/plot_mouse_footprints_opening_only_old_pvalues_PFM_sim.pdf",
# width = hm_width_pdf, height = hm_height_pdf)
# }else{
# pdf("output/F5/plot_mouse_footprints_opening_only_old_pvalues_TF_sim.pdf",
# width = hm_width_pdf, height = hm_height_pdf)
# }
# }
tissue=c("PBL","spleen","CD8 memory","CD8 naive")
draw(Heatmap(tmp_all_old_pvals[,1:2],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[1],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec_all_old_pvals,
show_heatmap_legend = FALSE,
row_names_max_width = max_text_width(
rownames(tmp_all_old_pvals),
gp = gpar(fontsize = 10))) +
Heatmap(tmp_all_old_pvals[,3:4],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[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(tmp_all_old_pvals),
gp = gpar(fontsize = 10)),
col=colvec_all_old_pvals) +
Heatmap(tmp_all_old_pvals[,5:6],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[3],
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(tmp_all_old_pvals),
gp = gpar(fontsize = 10)),
col=colvec_all_old_pvals) +
Heatmap(tmp_all_old_pvals[,7:8],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[4],
column_title_gp = gpar(fontsize = 10),
#column_names_gp = gpar(fontsize = 7),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
show_heatmap_legend = FALSE,
row_names_max_width = max_text_width(
rownames(tmp_all_old_pvals),
gp = gpar(fontsize = 10)),
col=colvec_all_old_pvals),
padding = unit(c(2, 2, 2, 20), "mm"))
draw(legend_p, just = c("right", "bottom"),
x = unit(0.965, "npc"), y = unit(0.05, "npc"))
# if(produce_plots_in_pdf){
# dev.off()
# }
```
```{r write_xlsx_for_merged, include=TRUE, echo=TRUE}
formatted_merged_hm <- vector("list", 2)
names(formatted_merged_hm) <- c("Fold change", "log p-values (FDR)")
formatted_merged_hm[[1]] <- tmp_all_old
formatted_merged_hm[[2]] <- tmp_all_old_pvals
tissue=c("PBL","spleen","CD8 memory","CD8 naive")
formatted_merged_w_tissue <- lapply(formatted_merged_hm, function(x, tis){
colnames(x) <- paste(rep(tis, each = 2), colnames(x))
y <- as.data.frame(x)
y$`Transcription factor` <- rownames(y)
y <- y[,c(ncol(y), c(1:(ncol(y)-1)))]
y$`Transcription factor` <- gsub("\n ", "", y$`Transcription factor`)
return(y)
}, tis = tissue)
file_name_merged <- "mouse_merged_transcription_factors_opening_FC_p.xlsx"
#write_xlsx(formatted_merged_w_tissue, path = paste0("output/F5/", file_name_merged))
```
## Plots FC and pv heatmaps, trees, and produce merged spreadsheet
```{r filter_to_old, include=TRUE, echo=TRUE}
tissue <- c("PBL", "spleen", "CD8+ memory", "CD8+ naive")
pval_cutoff <- -log(pq_value_cutoff) # -log10(0.05)
starting_FC <- starting_heatmap
starting_FC_df <- data.frame(starting_FC)
colnames(starting_FC_df) <- colnames(starting_FC)
starting_FC_df <- cbind(`Transcription factor` = rownames(starting_FC_df), starting_FC_df)
starting_pv <- df_heat_pvals
only_sig_FC_pv <- keep_only_significant_FC_pv(starting_FC_df, starting_pv,
filter_by = "18mo",
threshold = pval_cutoff)
colors_fc_p <- list(fc = c("#ffe6ed", "#c70039") , pv = c("#fff1e6", "#e66700"))
filtered_merged_tf <- vector("list", 2)
for(i in 1:2){
FC_or_p <- ifelse(i == 1, "fc", "pv")
matrix_of_vals <- as.matrix(only_sig_FC_pv[[i]][,2:ncol(only_sig_FC_pv[[i]])])
rownames(matrix_of_vals) <- only_sig_FC_pv[[i]][,1]
clustered <- cluster_heatmaps_on_TF_similarity(matrix_of_vals,
return_dend = TRUE)
mouse_opening <- clustered
save(mouse_opening, file = paste0("data/mouse_aging_footprint/mouse_opening_tree_",FC_or_p,".RData"))
df_hm_formatted_rn <- clustered[[1]]
filtered_merged_tf[[i]] <- clustered[[1]]
rownames(df_hm_formatted_rn) <- sapply(rownames(df_hm_formatted_rn),
introduce_new_line_to_rownames,
new_line_after = 30)
colnames(df_hm_formatted_rn) <- sapply(colnames(df_hm_formatted_rn), remove_mouse_tissue_from_colname)
if(produce_plots_in_pdf){
pdf(paste0("output/F5/mouse/mouse_filtered_opening_old_merged_PFM_", FC_or_p,".pdf"),
width = 8, height = 7)
}
colvec_sig_old_FC=colorRamp2(c(quantile(df_hm_formatted_rn,0.05),
quantile(df_hm_formatted_rn,0.95)),
#
colors_fc_p[[i]])
legend_only_sig_FC <- (Legend(col_fun = colvec_sig_old_FC,
title = ifelse(i == 1, "Fold change", "Log p value"),
legend_height = unit(4, "cm")))
draw(Heatmap(df_hm_formatted_rn[,1:2],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[1],
column_title_gp = gpar(fontsize = 10),
row_title="Transcription factors",
show_row_names=TRUE,
show_column_names = TRUE,
col=colvec_sig_old_FC,
show_heatmap_legend = FALSE,
row_names_max_width = max_text_width(
rownames(df_hm_formatted_rn),
gp = gpar(fontsize = 10))) +
Heatmap(df_hm_formatted_rn[,3:4],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[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(df_hm_formatted_rn),
gp = gpar(fontsize = 10)),
col=colvec_sig_old_FC) +
Heatmap(df_hm_formatted_rn[,5:6],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[3],
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(df_hm_formatted_rn),
gp = gpar(fontsize = 10)),
col=colvec_sig_old_FC) +
Heatmap(df_hm_formatted_rn[,7:8],
cluster_columns=F,
cluster_rows=F,
column_title=tissue[4],
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(df_hm_formatted_rn),
gp = gpar(fontsize = 10)),
col=colvec_sig_old_FC),
padding = unit(c(2, 2, 2, 20), "mm"))
draw(legend_only_sig_FC, just = c("right", "bottom"),
x = unit(0.965, "npc"), y = unit(0.05, "npc"))
if(produce_plots_in_pdf){
dev.off()
}
#if(produce_plots_in_pdf){
holder <- produce_dendrogram_plot(clustered[[3]],
length(unique(clustered[[2]])),
path = paste0("output/F5/mouse/mouse_opening_filtered_tree_",
i, ".pdf"),
subtitle_ = "Mouse, opening",
png = FALSE)
#}
}
# produce spreadsheet
merged_filtered_old <- lapply(filtered_merged_tf, function(x){
y <- as.data.frame(x)
y_n <- cbind(`Transcription factor` = rownames(x), y)
colnames(y_n) <- gsub("_log p value ", " ", colnames(y_n))
return(y_n)
})
names(merged_filtered_old) <- c("Fold change", "log p-values (FDR)")
file_name_merged_filtered <- "mouse_merged_filtered_transcription_factors_opening_FC_p.xlsx"
write_xlsx(merged_filtered_old, path = paste0("output/F5/mouse/", file_name_merged_filtered))
```
```{r opening_in_old_sig_per_tissue, include=TRUE, echo=TRUE}
log_p_thr <- -log(pq_value_cutoff) #-log10(0.05)
tissue <- c("PBL", "spleen", "memory", "naive")
find_sig_per_tissue <- vector("list", length = length(tissue))
for(i in 1:length(tissue)){
#tissue_cols <- c(1, grep(tissue[i], colnames(only_sig_FC_pv[["df_pv"]])))
fold_changes <- only_relevant_cols(only_sig_FC_pv[["df_FC"]], tissue[i])
log_p_values <- only_relevant_cols(only_sig_FC_pv[["df_pv"]], tissue[i])
relevant <- keep_only_significant_FC_pv(fold_changes,
log_p_values,
FALSE,
threshold = log_p_thr)
relevant_names <- split_jaspar_name_return_df_of_names(as.character(relevant[[1]][,1]))
find_sig_per_tissue[[i]] <- list(short_name = sort(unique(relevant_names$name)),
id = sort(unique(toupper(relevant_names$`Transcription factor`))))
}
```
# Venn diagram: opening peaks
```{r plot_venn_opening, include=TRUE, echo=TRUE}
source("code/library_function_wf_flow.R")
# non filtered values
# opening0=opening
#
# opening0 <- opening
#
# closing0=closing
# filtered values
opening0 <- lapply(find_sig_per_tissue, "[[", 1)
names(opening0) <- c("PBL", "spleen", "CD8+ memory", "CD8+\nnaive")
save(opening0, file = "data/mouse_aging_footprint/opening0.RData")
if(produce_plots_in_pdf){
pdf(file = "output/F5/mouse/mouse_venn_opening.pdf",
width = 7, height = 5)
}
txt_size <- 2
number_size <- 20
venn.alpha <- Venn(Sets = opening0)
gp.alpha <- compute.Venn(venn.alpha, type = "ChowRuskey", doWeights = T)
gp.a <- VennThemes(gp.alpha)
gp_ <- VennThemes(gp.alpha,increasingLineWidth=FALSE)
# modify tissue font size
gp_[["SetText"]][["Set1"]]$cex <- txt_size
gp_[["SetText"]][["Set1"]]$col <- color_values[which(names(color_values) == "PBL")]
gp_[["Set"]][["Set1"]]$col <- color_values[which(names(color_values) == "PBL")]
gp_[["SetText"]][["Set2"]]$cex <- txt_size
gp_[["SetText"]][["Set2"]]$col <- color_values[which(names(color_values) == "spleen")]
gp_[["Set"]][["Set2"]]$col <- color_values[which(names(color_values) == "spleen")]
gp_[["SetText"]][["Set3"]]$cex <- txt_size
gp_[["SetText"]][["Set3"]]$col <- color_values[which(names(color_values) == "CD8.memory")]
gp_[["Set"]][["Set3"]]$col <- color_values[which(names(color_values) == "CD8.memory")]
gp_[["SetText"]][["Set4"]]$cex <- txt_size
gp_[["SetText"]][["Set4"]]$col <- color_values[which(names(color_values) == "CD8.naive")]
gp_[["Set"]][["Set4"]]$col <- color_values[which(names(color_values) == "CD8.naive")]
# modify tissue label positions
labs <- VennGetSetLabels(gp.alpha)
labs$x <- c(-2.8, 3.5, 1.7, -4.6) #c("PBL", "spleen", "CD8+ memory", "CD8+\nnaive")
labs$y <- c(2.3, 0.4, -3.3, -2) # c("PBL", "spleen", "CD8+ memory", "CD8+\nnaive")
gp.alpha <- VennSetSetLabels(gp.alpha, labs)
############################
# modify intersect numbers
gp_[["FaceText"]][["1100"]]$fontsize <- number_size
gp_[["Face"]][["1100"]]$fill <- "#EE7600" #FF5733" #
gp_[["FaceText"]][["1111"]]$fontsize <- number_size
gp_[["Face"]][["1111"]]$fill <- "#c70039"#"#C70039"
gp_[["FaceText"]][["1110"]]$fontsize <- number_size
gp_[["Face"]][["1110"]]$fill <- "#FFC300"
gp_[["FaceText"]][["0111"]]$fontsize <- number_size
gp_[["Face"]][["0111"]]$fill <- "#F87531" #FF5733"
# modify intersect numbers positions
#labs_intersect <- VennGetFaceLabels(gp.alpha)
#labs_intersect$x <- c(-4, 3.5, 2, 0) # naive, spleen, memory, pbl, bckgrnd
#x
gp.alpha@ FaceLabels[,3] <- c(-2.5, 3.5, 2.2, 0, 0) #labs_intersect$x
#labs_intersect$y <- labs_intersect$y
# y
gp.alpha@ FaceLabels[,4] <- c(-2,-0.8,-2.24,0,0) #labs_intersect$y
intersect_all <- Reduce(intersect, opening0)
intersect_CD8M_CD8N_minus_all <- setdiff(intersect(opening0$`CD8+ memory`,
opening0$`CD8+
naive`), intersect_all)
intersect_PBL_CD8M_minus_all <- setdiff(intersect(opening0$PBL, opening0$`CD8+ memory`), intersect_all)
intersect_PBL_spleen_minus_all <- setdiff(intersect(opening0$PBL, opening0$spleen), intersect_all)
full_bottom_text <- paste0("Intersect of all (", length(intersect_all), "): ", introduce_new_line_to_rownames(paste0(intersect_all, collapse = ","),40), "\n",
"Intersect of PBL & spleen minus all (", length(intersect_PBL_spleen_minus_all), "): ",
introduce_new_line_to_rownames(paste0(intersect_PBL_spleen_minus_all, collapse = ","), 60), "\n",
"Intersect of PBL & CD8+ memory minus all (", length(intersect_PBL_CD8M_minus_all), "): ",
introduce_new_line_to_rownames(paste0(intersect_PBL_CD8M_minus_all, collapse = ","), 60), "\n",
"Intersect of CD8+ minus all (",length(intersect_CD8M_CD8N_minus_all),"): ",
introduce_new_line_to_rownames(paste0(intersect_CD8M_CD8N_minus_all, collapse = ","), 60))
grid.newpage()
plot(gp.alpha,
gp = gp_,
show=list(Universe=FALSE,
DarkMatter = FALSE))
#grid.newpage()
gridExtra::grid.arrange(grid::grid.grabExpr(plot(gp.alpha,
gp = gp_,
show=list(Universe=FALSE))),
top=textGrob(paste0("Opening peaks in young mice, log p value threshold: ", round(log_p_thr, digits = 3)),
gp=gpar(fontsize=15)),
bottom = textGrob(paste0(full_bottom_text)))
if(produce_plots_in_pdf){
dev.off()
}
```
```{r opening_spleen_mem_naive, include=FALSE, echo=FALSE}
# plot subsets of opening
opening_sub <- opening0
# remove PBL
opening_spleen_mem_nai <- opening_sub
opening_spleen_mem_nai[[1]]=NULL
venn.alpha_sp_me_na <- Venn(SetNames = tissue[-1],Sets = opening_spleen_mem_nai)
gp.alpha_sp_me_na <- compute.Venn(venn.alpha_sp_me_na)
gp.a_sp_me_na <- VennThemes(gp.alpha_sp_me_na)
plot(venn.alpha_sp_me_na, show=list(Universe=FALSE))
```
```{r opening_pbl_spleen, include=TRUE, echo=TRUE}
opening_pbl_spleen=opening_sub
opening_pbl_spleen[[3]]=opening_pbl_spleen[[4]]=NULL
venn.alpha_pbl_spleen <- Venn(SetNames = tissue[-c(3:4)],Sets = opening_pbl_spleen)
gp.alpha_pbl_spleen <- compute.Venn(venn.alpha_pbl_spleen)
gp.a_pbl_spleen <- VennThemes(gp.alpha_pbl_spleen)
plot(venn.alpha_pbl_spleen, show=list(Universe=FALSE))
```
# Venn diagram: closing peaks
```{r prepare_closing0, echo=TRUE, include=TRUE}
tissue=c("PBL","spleen","memory","naive")
closing0=vector("list",length(tissue))
names(closing0)=tissue
for(i in 1:length(tissue)){
df_of_young <- only_relevant_cols(list_of_peaks_per_tissue[["closing"]][[i]], "3mo")
df_of_young_fc <- only_relevant_cols(df_of_young, "FC")
df_of_young_pv <- only_relevant_cols(df_of_young, "p value")
filtered_dfs <- keep_only_significant_FC_pv(df_of_young_fc,
df_of_young_pv,
FALSE,
threshold = log_p_thr)
nice_names <- split_jaspar_name_return_df_of_names(filtered_dfs$df_FC$`Transcription factor`)
closing0[[i]] <- list(short_name = unique(toupper(nice_names$name)),
jasp_id = unique(toupper(nice_names$`Transcription factor`)))
}
```
```{r plot_venn_closing, include=TRUE, echo=TRUE}
closing0_short <- lapply(closing0, "[[", 1)
names(closing0_short) <- c("PBL", "spleen", "CD8+ memory", "CD8+ naive")
number_size_c <- number_size
text_size_c <- txt_size
if(produce_plots_in_pdf){
pdf(file = "output/F5/mouse/mouse_venn_closing.pdf",
width = 7, height = 5)
}
venn.alpha_closing <- Venn(Sets = closing0_short)
gp.alpha_closing <- compute.Venn(venn.alpha_closing)
gp.a_closing <- VennThemes(gp.alpha_closing)
gp_c <- VennThemes(gp.alpha_closing,increasingLineWidth=FALSE)
# modify tissue font size
gp_c[["SetText"]][["Set1"]]$cex <- text_size_c
gp_c[["SetText"]][["Set2"]]$cex <- text_size_c
gp_c[["SetText"]][["Set3"]]$cex <- text_size_c
gp_c[["SetText"]][["Set4"]]$cex <- text_size_c
gp_c[["SetText"]][["Set1"]]$col <- color_values[which(names(color_values) == "PBL")]
gp_c[["Set"]][["Set1"]]$col <- color_values[which(names(color_values) == "PBL")]
gp_c[["SetText"]][["Set2"]]$col <- color_values[which(names(color_values) == "spleen")]
gp_c[["Set"]][["Set2"]]$col <- color_values[which(names(color_values) == "spleen")]
gp_c[["SetText"]][["Set3"]]$col <- color_values[which(names(color_values) == "CD8.memory")]
gp_c[["Set"]][["Set3"]]$col <- color_values[which(names(color_values) == "CD8.memory")]
gp_c[["SetText"]][["Set4"]]$col <- color_values[which(names(color_values) == "CD8.naive")]
gp_c[["Set"]][["Set4"]]$col <- color_values[which(names(color_values) == "CD8.naive")]
# modify tissue label positions
labs_c <- VennGetSetLabels(gp.alpha_closing)
labs_c$x <- c(1.3, 0.2, -1.5, -6) #c("PBL", "spleen", "CD8+ memory", "CD8+\nnaive")
labs_c$y <- c(0, -2.5, 3, 0.4)#c(1.1, 0.4, -3.3, -2) # c("PBL", "spleen", "CD8+ memory", "CD8+\nnaive")
gp.alpha_closing <- VennSetSetLabels(gp.alpha_closing, labs_c)
gp_c[["FaceText"]][["1100"]]$fontsize <- number_size
gp_c[["Face"]][["1100"]]$fill <- "#284D64"#133C55" #very dark #364B7F" #"#4C5F8D"#0A2463"
gp_c[["FaceText"]][["0010"]]$fontsize <- number_size
gp_c[["Face"]][["0010"]]$fill <- "#84D2F6"#30ADF2"
gp_c[["FaceText"]][["0011"]]$fontsize <- number_size
gp_c[["Face"]][["0011"]]$fill <- "#386FA4" #also dark #"#285D82"#3E92CC"
gp_c[["FaceText"]][["0001"]]$fontsize <- number_size
gp_c[["Face"]][["0001"]]$fill <- "#59A5D8" #9BC4E2"
gp_c[["FaceText"]][["0100"]]$fontsize <- number_size
gp_c[["Face"]][["0100"]]$fill <-"#91E5F6" #"#3A75C4"
# modify intersect numbers positions
#labs_intersect <- VennGetFaceLabels(gp.alpha)
#labs_intersect$x <- c(-4, 3.5, 2, 0) # naive, spleen, memory, pbl, bckgrnd
#x
gp.alpha_closing@ FaceLabels[,3] <- c(-4, -2.5, -1.3, 0.33, 1, 2)
#labs_intersect$x
#labs_intersect$y <- labs_intersect$y
# y
gp.alpha_closing@ FaceLabels[,4] <- c(-1, 2.5, 0.4, -1, -0.25, 0)
grid.newpage()
plot(gp.alpha_closing,
gp = gp_c,
show=list(Universe=FALSE,
DarkMatter = FALSE))
#closing0_short
intersect_PBL_spleen <- intersect(closing0_short$PBL, closing0_short$spleen)
closing_spleen_only <- setdiff(closing0_short$spleen, intersect_PBL_spleen)
intersect_CD8 <- intersect(closing0_short$`CD8+ memory`, closing0_short$`CD8+ naive`)
closing_CD8M_only <- setdiff(closing0_short$`CD8+ memory`, intersect_CD8)
closing_CD8N_only <- setdiff(closing0_short$`CD8+ naive`, intersect_CD8)
closing_bottom_overlaps <- paste0("CD8+ naive only (", length(closing_CD8N_only), "): ", paste0(closing_CD8N_only, collapse = ", "), "\n",
"CD8+ memory only (", length(closing_CD8M_only),"): ", paste0(closing_CD8M_only, collapse = ", "), "\n",
"Spleen only (", length(closing_spleen_only), "): ", paste0(closing_spleen_only, collapse = ", "), "\n",
"Intersect of CD8+ (", length(intersect_CD8), "): ", paste0(intersect_CD8, collapse = ", "), "\n",
"Intersect of PBL and spleen (", length(intersect_PBL_spleen), "): ", paste0(intersect_PBL_spleen, collapse = ", "))
gridExtra::grid.arrange(grid::grid.grabExpr(plot(gp.alpha_closing,
gp = gp_c,
show=list(Universe=FALSE))),
top=textGrob(paste0("Closing peaks in young, log p value threshold: ", round(log_p_thr, digits = 3)),
gp=gpar(fontsize=15)),
bottom = textGrob(closing_bottom_overlaps))
if(produce_plots_in_pdf){
dev.off()
}
```
```{r plot_venn_spleen_mem_naive, include=FALSE, echo=FALSE}
closing_sub <- closing0
# closing_spleen_mem_naive <- closing_sub
# closing_spleen_mem_naive[[1]]=NULL
# venn.alpha_c_spleen_mem_naive <- Venn(SetNames = tissue[-1],Sets = closing_spleen_mem_naive)
# gp.alpha_c_spleen_mem_naive <- compute.Venn(venn.alpha_c_spleen_mem_naive)
# gp.a <- VennThemes(gp.alpha_c_spleen_mem_naive)
# plot(venn.alpha_c_spleen_mem_naive, show=list(Universe=FALSE))