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Figure5_3_overlap.Rmd
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---
title: "Human and mouse TF overlap"
author: "Lori D Kregar, Ahrim Youn, ..., Duygu Ucar"
date: "2019-06-17"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Prepare the environment
```{r setup, include=FALSE}
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!
```
```{r libraries, echo=TRUE, include=FALSE}
library(ComplexHeatmap, quietly = TRUE)
library(circlize, quietly = TRUE)
library(graph, quietly = TRUE)
library(RBGL, quietly = TRUE)
library(Vennerable, quietly = TRUE)
library(dplyr, quietly = TRUE)
library(ggplot2)
color_values <- source("code/color_values.R")
```
```{r useful_functions, include=TRUE, echo=TRUE}
put_plots_to_pdf <- TRUE
source("code/TF_pfm_extension_functions.R")
extract_peaktype_per_age_or_gender <- function(df_per_tissue, age_or_gender, thresh){
#df_per_tissue <- mouse_peaks_per_tissue[["opening"]][[1]]
only_FC <- only_relevant_cols(df_per_tissue, "FC")
only_P <- only_relevant_cols(df_per_tissue, "p value")
only_sig <- keep_only_significant_FC_pv(only_FC, only_P, age_or_gender, thresh)
return(only_sig)
}
names_from_mice <- function(x){
tfs <- x[[1]][["Transcription factor"]]
tfs_clean <- sapply(tfs, function(y){
return(substr(y, 8, nchar(y)))
})
return(tfs_clean)
}
mouse_filter_peaktype_age <- function(df_from_xlsx, peaktype, age, log_p_limit){
peaks_per_tissue <- lapply(df_from_xlsx[[peaktype]],
extract_peaktype_per_age_or_gender,
age_or_gender = age, thresh = log_p_limit)
only_names_opening_mice_old_per_tissue <- lapply(peaks_per_tissue, names_from_mice)
}
human_filter_peaktype_age <- function(df_like_in_xlsx, peaktype, age, log_p_limit){
peaktype_human <- extract_peaktype_per_age_or_gender(df_like_in_xlsx[[peaktype]][[1]],
age_or_gender = age,
thresh = log_p_limit)
only_names_human <- sapply(peaktype_human[[1]][["Transcription factor"]],
function(x){
return(substr(x, 8, nchar(x)))
})
return(only_names_human)
}
replicate_into_list <- function(df, times, df_name){
new_list <- vector("list", times)
for(i in 1:times){
new_list[[i]] <- df
}
names(new_list) <- rep(df_name, times)
return(new_list)
}
```
```{r load_data, echo=TRUE, include=TRUE}
log_p_value_treshold <- -log(0.05)
load("data/mouse_aging_footprint/mouse_TF_FC_p_xlsx.Rdata")
mouse_peaks_per_tissue <- list_of_peaks_per_tissue
load("data/human_aging_footprint/human_TF_FC_p_xslx.Rdata")
human_peaks <- opening_closing_human
```
# Opening peaks
```{r preprocess_opening, echo=TRUE, include=TRUE}
# opening_mice_old
opening_mice_old <- mouse_filter_peaktype_age(mouse_peaks_per_tissue,
"opening",
"18mo",
log_p_value_treshold)
# opening_human_old
opening_old_human <- human_filter_peaktype_age(human_peaks,
"opening",
" O",
log_p_value_treshold)
human_opening_PBMC <- replicate_into_list(opening_old_human, length(opening_mice_old), "PBMC")
# REPLACE JUNFOS BY FOSJUN in mice
only_names_opening_mice_old <- lapply(opening_mice_old,
function(x){
x[(x == "JUNFOS")] <- "FOSJUN"
return(x)
})
# draw Venn diagrams per tissue
if(put_plots_to_pdf){
pdf(file = "output/F5/overlap/opening_human_mice_overlap.pdf")
}
for(i in 1:length(only_names_opening_mice_old)){
groups <- c(names(human_opening_PBMC)[i], names(only_names_opening_mice_old)[i])
sets <- list(human_opening_PBMC[[i]], only_names_opening_mice_old[[i]])
sets_clean <- lapply(sets, function(x){
return(unique(toupper(x)))
})
names(sets_clean) <- groups
# print("##############################")
# print(setdiff(sets_clean[[1]], sets_clean[[2]]))
# print("###")
# print(intersect(sets_clean[[1]], sets_clean[[2]]))
# print("###")
# print(setdiff(sets_clean[[2]], sets_clean[[1]]))
#
venn.alpha <- Venn(Sets = sets_clean)
# gp.alpha <- compute.Venn(venn.alpha)
# gp.a <- VennThemes(gp.alpha)
#plot(venn.alpha)
#plot(gp.a)
names_in_intersect <- paste0("Intersect: ", paste0(sort(intersect(sets_clean[[1]], sets_clean[[2]])),
#sep = "",
collapse = ", "))
gridExtra::grid.arrange(grid::grid.grabExpr(plot(venn.alpha, show=list(Universe=FALSE))),
top=textGrob(paste0("Opening peaks in old, log p threshold: ",
round(log_p_value_treshold, digits = 3)),
gp=gpar(fontsize=15)),
bottom = textGrob(names_in_intersect, gp=gpar(fontsize=12)))
}
if(put_plots_to_pdf){
dev.off()
}
opening_sig_mice <- c("RUNX1","RUNX2","RUNX3",
"ATF1","ATF7","CREB1","CREB3","CREM","GMEB2","JDP2","XBP1",
"ELF1","ERF","ETS1","ETV6","FLI1",
"BACH1MAFK","FOS","FOSL1","FOSL2","JUN","JUNB","JUND","JUNFOS","NFE2L2",
"NFKB1","NFKB2","REL","RELA",
"MGA","RFX1","RFX2","RFX5",
"IRF8")
all_opening_mice <- unique(toupper(unlist(only_names_opening_mice_old)))
all_opening_human <- unique(toupper(unlist(human_opening_PBMC)))
all_opening <- list(mouse = opening_sig_mice,
human = all_opening_human)
venn_opening_all <- Venn(Sets = all_opening)
gp.alpha <- compute.Venn(venn_opening_all)
gp_ <- VennThemes(gp.alpha,increasingLineWidth=FALSE)
gp_[["Face"]][["11"]]$fill <- "#c70039" #SET FILL
gp_[["Face"]][["10"]]$fill <- "#EE7600" #SET FILL
gp_[["Face"]][["01"]]$fill <- "#FFB00F" #SET FILL
#plot(venn_opening_all)
intersect_opening <- paste0("Intersect: ", paste0(sort(intersect(all_opening[[1]], all_opening[[2]])),
collapse = ", "))
if(put_plots_to_pdf){
pdf(file = "output/F5/overlap/opening_human_mice_overlap_all.pdf",
width = 5, height = 4)
}
gridExtra::grid.arrange(grid::grid.grabExpr(plot(venn_opening_all, gp = gp_, show=list(Universe=FALSE))),
top=textGrob(paste0("Opening peaks associated TFs in\naged humans and mice"#,
#round(log_p_value_treshold, digits = 3)
),
gp=gpar(fontsize=15)),
bottom = textGrob((intersect_opening), gp=gpar(fontsize=12)))
if(put_plots_to_pdf){
dev.off()
}
```
# Closing peaks
```{r closing_young_overlap, include=TRUE, echo=TRUE}
closing_mice_young <- mouse_filter_peaktype_age(mouse_peaks_per_tissue,
"closing",
"3mo",
log_p_value_treshold)
only_names_closing_human <- human_filter_peaktype_age(human_peaks,
"closing",
" Y",
log_p_value_treshold)
human_closing_PBMC <- replicate_into_list(only_names_closing_human,
length(closing_mice_young), "PBMC")
# draw Venn
if(put_plots_to_pdf){
pdf(file = "output/F5/overlap/closing_human_mice_overlap.pdf")
}
for(i in 1:length(closing_mice_young)){
groups <- c(names(human_closing_PBMC)[i], names(closing_mice_young)[i])
sets <- list(human_closing_PBMC[[i]], closing_mice_young[[i]])
sets_clean <- lapply(sets, function(x){
return(unique(toupper(x)))
})
names(sets_clean) <- groups
venn.alpha <- Venn(Sets = sets_clean)
# gp.alpha <- compute.Venn(venn.alpha)
# gp.a <- VennThemes(gp.alpha)
names_formatted <- paste0("Intersect: ", paste0(sort(intersect(sets_clean[[1]], sets_clean[[2]])),
collapse = ", "))
gridExtra::grid.arrange(grid::grid.grabExpr(plot(venn.alpha, show=list(Universe=FALSE))),
top=textGrob(paste0("Closing peaks in young, log p value threshold: ", round(log_p_value_treshold, digits = 3)),
gp=gpar(fontsize=15)),
bottom = textGrob(names_formatted, gp=gpar(fontsize=10)))
}
if(put_plots_to_pdf){
dev.off()
}
all_closing_human <- unique(toupper(unlist(human_closing_PBMC)))
all_closing_mice <- unique(toupper(unlist(closing_mice_young)))
intersect_closing <- intersect(all_closing_human, all_closing_mice)
intersect_human_close_mouse_open <- sort(intersect(all_closing_human, all_opening$mouse))
```
```{r intersect_venn_closing, include=TRUE, echo=TRUE}
# opening_mice_old
closing_mice_old <- mouse_filter_peaktype_age(mouse_peaks_per_tissue,
"closing",
"3mo",
log_p_value_treshold)
# opening_human_old
closing_old_human <- human_filter_peaktype_age(human_peaks,
"closing",
" Y",
log_p_value_treshold)
combined_closing_mice <- unique(toupper(unlist(closing_mice_old)))
combined_closing_human <- unique(toupper(unlist(closing_old_human)))
overlap_combined_closing <- list(mouse = combined_closing_mice,
human = combined_closing_human)
venn_closing_all <- Venn(Sets = overlap_combined_closing)
gp.alpha_closing <- compute.Venn(venn_closing_all)
gp_cl <- VennThemes(gp.alpha_closing,increasingLineWidth=FALSE)
gp_cl[["Face"]][["11"]]$fill <- "#386FA4" #SET FILL
gp_cl[["Face"]][["10"]]$fill <- "#84D2F6" #SET FILL
gp_cl[["Face"]][["01"]]$fill <- "#59A5D8" #SET FILL
#plot(venn_opening_all)
intersect_closing <- paste0("Intersect: ", paste0(sort(intersect(overlap_combined_closing[[1]],
overlap_combined_closing[[2]])),
collapse = ", "))
if(put_plots_to_pdf){
pdf(file = "output/F5/overlap/closing_human_mice_overlap_all.pdf",
width = 5, height = 4)
}
gridExtra::grid.arrange(grid::grid.grabExpr(plot(venn_closing_all,
gp = gp_cl, show=list(Universe=FALSE))),
top=textGrob(paste0("Closing peaks associated TFs in\naged humans and mice"#,
#round(log_p_value_treshold, digits = 3)
),
gp=gpar(fontsize=15)),
bottom = textGrob((introduce_new_line_to_rownames(intersect_closing)), gp=gpar(fontsize=12)))
if(put_plots_to_pdf){
dev.off()
}
```
```{r}
read_excel_allsheets <- function(filename, tibble = FALSE) {
# I prefer straight data.frames
# but if you like tidyverse tibbles (the default with read_excel)
# then just pass tibble = TRUE
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
}
# Define common tfs across species
common.tfs <- c("FOS", "FOSL2", "JDP2", "JUNB", "JUND", "NFE2L2")
mouse <- read_excel_allsheets("output/F5/mouse/opening_transcription_factors_FC_p_mouse.xlsx")
human <- read_excel("output/F5/human/opening_transcription_factors_FC_p_human.xlsx")
#iterate over tissue/cell type
plot.tfs.mice <- lapply(mouse, function(tct){
tfs <- tct[(tct$name %in% common.tfs),]
fc.b6 <- tfs$`FC B6 18mo`
fc.nzo<- tfs$`FC NZO 18mo`
res <- cbind(fc.b6, fc.nzo)
rownames(res) <- tfs$name
return(res)
}) %>% do.call(cbind, .)
idx.b6 <- seq(1,ncol(plot.tfs.mice), by = 2)
idx.nzo<- seq(2,ncol(plot.tfs.mice), by = 2)
df.tf.b6 <- plot.tfs.mice[,idx.b6]
df.tf.nzo<- plot.tfs.mice[,idx.nzo]
df.tf.b6 <- df.tf.b6[!duplicated(rownames(df.tf.b6)),]
df.tf.nzo<- df.tf.nzo[!duplicated(rownames(df.tf.nzo)),]
colnames(df.tf.b6) <- colnames(df.tf.nzo) <- names(mouse)
breaksList = seq(-5, 5, by = .001)
pheatmap(cbind(df.tf.b6, df.tf.nzo),
cluster_rows = F, cluster_cols = F, border_color = "white",
color = colorRampPalette((brewer.pal(n = 9, name = "Reds")))(length(breaksList)))
plot.tfs.human <- cbind(human[human$name %in% common.tfs,]$`FC F O`,
human[human$name %in% common.tfs,]$`FC M O`)
plot.tfs.human <- plot.tfs.human[!duplicated(rownames(plot.tfs.human)),]
rownames(plot.tfs.human) <- human$name[human$name %in% common.tfs]
colnames(plot.tfs.human) <- c("Female", "Male")
df.plot.tf <- cbind(plot.tfs.human, df.tf.b6, df.tf.nzo)
anno.col <- data.frame(Strain = c(rep(NA,2), rep("B6", 4), rep("NZO", 4)))
rownames(anno.col) <- colnames(df.plot.tf)
pdf("output/F5/overlap/human_mice_opening_heatmap.pdf", useDingbats = F, width = 6, height = 3)
breaksList = seq(-5, 5, by = .001)
pheatmap(df.plot.tf,
cluster_rows = F, cluster_cols = F, border_color = "white",
color = colorRampPalette((brewer.pal(n = 9, name = "Reds")))
(length(breaksList)), gaps_col = c(2,6))
dev.off()
```
```{r, shared TFs human&mice expressions FC}
common.tfs <- c("FOS", "FOSL2", "JDP2", "JUNB", "JUND", "NFE2L2")
common.tfs.ensembl <- c('ENSMUSG00000021250', 'ENSMUSG00000029135', 'ENSMUSG00000034271',
'ENSMUSG00000052837', 'ENSMUSG00000071076', 'ENSMUSG00000015839')
names(common.tfs.ensembl) <- common.tfs
mouse.files <- list.files("output/F3/DE Genes/All Genes/")
mouse.de.genes <- lapply(mouse.files, function(x){
read.csv(file = paste0("output/F3/DE Genes/All Genes/", x))
})
names(mouse.de.genes) <- mouse.files
mouse.de.genes <- dplyr::bind_rows(mouse.de.genes, .id = "TCT")
mouse.de.genes$Strain <- sapply(mouse.de.genes$TCT, function(x){
strsplit(x, "_", fixed = TRUE)[[1]][3]
})
mouse.de.genes$TCT <- sapply(mouse.de.genes$TCT, function(x){
strsplit(x, "_", fixed = TRUE)[[1]][1]
})
colnames(mouse.de.genes)[2] <- "Gene.Ensembl"
mouse.de.genes.filtered <- mouse.de.genes[mouse.de.genes[,2] %in% common.tfs.ensembl,]
mouse.de.genes.filtered <- mouse.de.genes.filtered %>% arrange(Strain, TCT, Gene.Ensembl)
pheatmap.values <- matrix(mouse.de.genes.filtered$logFC, nrow = length(unique(mouse.de.genes.filtered$Gene.Ensembl)))
rownames(pheatmap.values) <- names(sort(common.tfs.ensembl))
colnames(pheatmap.values) <- rep(unique(mouse.de.genes.filtered$TCT),2)
######### Add human counterparts
rna.de.genes <- read_excel("data/RNAseq/human/RNAseq_DE_eladio.xlsx")
rna.de.genes <- rna.de.genes[rna.de.genes$Contrast == "HOxHY",]
rna.de.genes.m <- rna.de.genes %>% filter(Sex == "Males")
rna.de.genes.f <- rna.de.genes %>% filter(Sex == "Females")
# Load Biomart DB
# Notice that this database is for human, to see different DBs:
# mart = useMart("ensembl"); listDatasets(mart)
mart <- useMart("ensembl",
host = "useast.ensembl.org",
dataset = "hsapiens_gene_ensembl")
ensembl2geneID <- function(x) {
# Get the mapping matrix for ensembl_gene_id to hgnc_symbol!
mapping <- getBM(mart = mart,
useCache = T, uniqueRows = T,
filters = "ensembl_gene_id",
values = x$EnsemblID,
attributes = c("ensembl_gene_id","hgnc_symbol"))
a<-duplicated(mapping)
mapping <- mapping[!a,]
y <- merge(x, mapping, by.x = "EnsemblID", by.y = "ensembl_gene_id", all.x = T, sort = F)
return (y)
}
rna.de.genes.m <- ensembl2geneID(rna.de.genes.m)
rna.de.genes.f <- ensembl2geneID(rna.de.genes.f)
pheatmap.values <- cbind(
rna.de.genes.f[ match( names(sort(common.tfs.ensembl)),rna.de.genes.f$hgnc_symbol),]$logFC,
rna.de.genes.m[ match( names(sort(common.tfs.ensembl)),rna.de.genes.m$hgnc_symbol),]$logFC,
pheatmap.values)
colnames(pheatmap.values)[c(1,2)] <- c("Female", "Male")
breaksList = seq(-5, 5, by = .001)
pdf("output/F5/human_mice_RNAseq_FC_common_TFs.pdf", usingDingbats = F,
width = 5, height = 3)
pheatmap(pheatmap.values,
cluster_rows = F, cluster_cols = F, border_color = "white",
color = colorRampPalette((brewer.pal(n = 9, name = "Reds")))(length(breaksList)),
# color = viridis(10),
gaps_col = c(2,6))
dev.off()
```