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Figure4-Human_CD8_JEM.rmd
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Figure4-Human_CD8_JEM.rmd
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---
title: "Human CD8 Analyses"
author: "EO Karakaslar"
date: "12/2/2020"
output: html_document
---
```{r setup, include=FALSE}
require(knitr)
knitr::opts_chunk$set(echo = TRUE)
opts_knit$set(root.dir = "/Users/karako/Dropbox (JAX)/MouseAging_clean/") #set root dir!
```
```{r libraries}
library(dplyr)
library(cinaR)
library(readxl)
```
```{r}
meta.cd8 <- read_excel("data/ATACseq/human/Human_CD8_meta.xls")
data.mem <- read.csv("data/ATACseq/human/MEMORYCD8T_8_samples_raw.txt", sep = "\t")
data.nai <- read.csv("data/ATACseq/human/NAIVECD8T_8_samples_raw.txt", sep = "\t")
data.mem <- data.mem[,c(1:11)]
data.nai <- data.nai[,c(1:11)]
colnames(data.mem) <- strsplit(colnames(data.mem), "_", fixed = TRUE )%>% sapply(function(x){x[1]})
colnames(data.nai) <- strsplit(colnames(data.nai), "_", fixed = TRUE )%>% sapply(function(x){x[1]})
# Remove D0 from sample name
meta.cd8$sampid[meta.cd8$sampid == "VHY402D0"] <- "VHY402"
meta.mem <- meta.cd8[match(colnames(data.mem[,c(4:11)]), meta.cd8$sampid),]
meta.nai <- meta.cd8[match(colnames(data.nai[,c(4:11)]), meta.cd8$sampid),]
load("data/genesets/Wikipathways/Wiki_inflammation.rda")
# make module GMT format
wp.inflammation.gmt <- split(wp.inflammation$GeneName, wp.inflammation$Module.Name)
```
```{r cinaR memory}
result.mem <- cinaR(data.mem, contrasts = meta.mem$group, reference.genome = "hg19",
batch.correction = F, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = wp.inflammation.gmt)
pdf("output/F4/Human_CD8/Memory_CD8_Results_without_batch.pdf", onefile = T)
dot_plot(result.mem, filter.pathways = T)
dev.off()
result.mem.batch <- cinaR(data.mem, contrasts = meta.mem$group, reference.genome = "hg19",
batch.correction = T, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = wp.inflammation.gmt)
pdf("output/F4/Human_CD8/Memory_CD8_Results_with_batch.pdf", onefile = T)
pca_plot(result.mem.batch, meta.mem$group)
dot_plot(result.mem.batch, filter.pathways = T)
dev.off()
```
```{r cinaR naive}
result.nai <- cinaR(data.nai, contrasts = meta.nai$group, reference.genome = "hg19",
batch.correction = F, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = wp.inflammation.gmt)
pdf("output/F4/Human_CD8/Naive_CD8_Results_without_batch.pdf", onefile = T)
dot_plot(result.nai, filter.pathways = T)
dev.off()
result.nai.batch <- cinaR(data.nai, contrasts = meta.nai$group, reference.genome = "hg19",
batch.correction = T, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = wp.inflammation.gmt)
pdf("output/F4/Human_CD8/Naive_CD8_Results_with_batch.pdf", onefile = T)
pca_plot(result.nai.batch, meta.nai$group)
dot_plot(result.nai.batch, filter.pathways = T)
dev.off()
```
```{r Pheatmap CD8 }
enrichment.df <- rbind(
cbind(CT = "CD8 Naive", result.nai$Enrichment.Results$HO_HY),
cbind(CT = "CD8 Memory", result.mem$Enrichment.Results$HO_HY)
)
p <- ggplot(subset(enrichment.df, adj.p < 0.1),
aes(
x = CT,
y = module.name,
size = ifelse(adj.p < 0.1,-log(adj.p),
NA),
color = status
)) + geom_point() + scale_color_manual(values = color_values) +
xlab("") + ylab("") + theme_minimal(base_size = 18) + labs(size = "-log(adj.p)", color = "Regulation")
ggsave(filename = "output/F4/Human_CD8/Enrichment_WP_Inflammation_Human.pdf", plot = p, width = 11.6, height = 6)
```
```{r}
DA.peaks.mem <- result.mem.batch$DA.results$DA.peaks$HO_HY
DA.peaks.nai <- result.nai.batch$DA.results$DA.peaks$HO_HY
write.csv(DA.peaks.mem, "output/F4/Human_CD8/DA_peaks_mem_batch_corrected.csv")
write.csv(DA.peaks.nai, "output/F4/Human_CD8/DA_peaks_nai_batch_corrected.csv")
```
```{r scRNAseq pbmc top module}
load("analysis/cache/enrichment_analysis.Rdata")
geneset <- merge(selected_genesets$scrnaseq_pbmc_top, selected_genesets_labels$scrnaseq_pbmc_top, by = "Module.ID")
geneset <- split(geneset$GeneName, geneset$Module.Name)
result.mem.batch <- cinaR(data.mem, contrasts = meta.mem$group, reference.genome = "hg19",
batch.correction = T, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = geneset)
result.nai.batch <- cinaR(data.nai, contrasts = meta.nai$group, reference.genome = "hg19",
batch.correction = T, DA.choice = 2, DA.fdr.threshold = 0.1,
geneset = geneset)
```
```{r}
# Works only if FDR = 1 & LFC = 0
create_peak_matrix <- function(result){
da <- result$DA.peaks$HO_HY
cp <- result$cp
# WOAH %<>%
da <- da %>% arrange(abs(distanceToTSS))
da <- da[!duplicated(da$gene_name) & !is.na(da$gene_name),]
cp <- cp[match(da$Row.names, rownames(cp)),]
rownames(cp) <- da$gene_name
return(cp)
}
```
```{r}
ap1.genes <- c("Jun", "Junb", "Jund", "Fos", "Fosb", "Fosl2", "Fra1" ,"Fra2", "Atf" ,"Atf2", "Atf3", "Atf4", "Atf5", "Atf6", "Atf6b", "Atf7", "Batf", "Batf2", "Batf3" , "Jdp2", "Maf", "Mafa", "Mafb", "Maff", "Mafg", "Mafk")
ap1.genes.human <- toupper(ap1.genes)
result.nai <- cinaR(data.nai, contrasts = meta.nai$group, reference.genome = "hg19",
batch.correction = F, DA.choice = 2, DA.fdr.threshold = 1,
run.enrichment = F)
# Prepare motif analyses files
DA.res <- result.nai$DA.peaks$HO_HY
colnames(DA.res)[c(1:4, 6)] <- c("UniqueID", "Chr", "Start", "End", "Strand")
DA.res$Strand <- "+"
nai.background <- DA.res[abs(DA.res$logFC) < 0.2, c("UniqueID", "Chr", "Start", "End", "Strand")]
nai.opening <- DA.res[DA.res$logFC > 0 & DA.res$FDR < 0.1, c("UniqueID", "Chr", "Start", "End", "Strand")]
nai.closing <- DA.res[DA.res$logFC < 0 & DA.res$FDR < 0.1, c("UniqueID", "Chr", "Start", "End", "Strand")]
write.table(x = nai.opening,
file = "output/F5/Homer Motif Analyses/Human CD8/Opening_Human_CD8_Naive_HomerReady.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = nai.closing,
file = "output/F5/Homer Motif Analyses/Human CD8/Closing_Human_CD8_Naive_HomerReady.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = nai.background,
file = "output/F5/Homer Motif Analyses/Human CD8/Opening_Human_CD8_Naive_HomerReady_Background.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = nai.background,
file = "output/F5/Homer Motif Analyses/Human CD8/Closing_Human_CD8_Naive_HomerReady_Background.txt",
quote = FALSE, sep = "\t", row.names = F)
cp.nai <- create_peak_matrix(result.nai)
cp.nai.ap1 <- cp.nai[rownames(cp.nai) %in% ap1.genes.human,order(meta.nai$age)]
meta.ordered <- meta.nai[order(meta.nai$age),]
annotation.col <- meta.ordered$age %>% as.numeric %>% data.frame
rownames(annotation.col) <- meta.ordered$sampid
colnames(annotation.col) <- c("Age")
breaksList = seq(-2, 2, by = .001)
pdf("output/F4/Heatmap_AP1_Genes_CD8_Naive_Human_ATACseq.pdf", width = 3.5, height = 4)
pheatmap(cp.nai.ap1, annotation_col = annotation.col,
scale = "row", breaks = breaksList, cluster_cols = F, show_colnames = F,
colorRampPalette(rev(brewer.pal(n = 9, name = "RdBu")))(length(breaksList)))
dev.off()
```
```{r}
result.mem <- cinaR(data.mem, contrasts = meta.mem$group, reference.genome = "hg19",
batch.correction = F, DA.choice = 2, DA.fdr.threshold = 1,
run.enrichment = F)
# Prepare motif analyses files
DA.res <- result.mem$DA.peaks$HO_HY
colnames(DA.res)[c(1:4, 6)] <- c("UniqueID", "Chr", "Start", "End", "Strand")
DA.res$Strand <- "+"
mem.background <- DA.res[abs(DA.res$logFC) < 0.2, c("UniqueID", "Chr", "Start", "End", "Strand")]
mem.opening <- DA.res[DA.res$logFC > 0 & DA.res$FDR < 0.1, c("UniqueID", "Chr", "Start", "End", "Strand")]
mem.closing <- DA.res[DA.res$logFC < 0 & DA.res$FDR < 0.1, c("UniqueID", "Chr", "Start", "End", "Strand")]
write.table(x = mem.opening,
file = "output/F5/Homer Motif Analyses/Human CD8/Opening_Human_CD8_Memory_HomerReady.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = mem.closing,
file = "output/F5/Homer Motif Analyses/Human CD8/Closing_Human_CD8_Memory_HomerReady.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = mem.background,
file = "output/F5/Homer Motif Analyses/Human CD8/Opening_Human_CD8_Memory_HomerReady_Background.txt",
quote = FALSE, sep = "\t", row.names = F)
write.table(x = mem.background,
file = "output/F5/Homer Motif Analyses/Human CD8/Closing_Human_CD8_Memory_HomerReady_Background.txt",
quote = FALSE, sep = "\t", row.names = F)
cp.mem <- create_peak_matrix(result.mem)
cp.mem.ap1 <- cp.mem[rownames(cp.mem) %in% ap1.genes.human,order(meta.mem$age)]
meta.ordered.mem <- meta.mem[order(meta.mem$age),]
annotation.col <- meta.ordered.mem$age %>% as.numeric %>% as.data.frame
rownames(annotation.col) <- meta.ordered.mem$sampid
colnames(annotation.col) <- c("Age")
breaksList = seq(-2, 2, by = .001)
pdf("output/F4/Heatmap_AP1_Genes_CD8_Memory_Human_ATACseq.pdf", width = 3.5, height = 4)
pheatmap(cp.mem.ap1, annotation_col = annotation.col,
scale = "row", breaks = breaksList, show_colnames = F, cluster_cols = F,
colorRampPalette(rev(brewer.pal(n = 9, name = "RdBu")))(length(breaksList)))
dev.off()
```
```{r FOSL2}
source("code/color_values.R")
df.plot.nai <- cbind(expr = cp.nai["FOSL2", ], meta.nai, cell.type = "CD8+ Naive")
df.plot.mem <- cbind(expr = cp.mem["FOSL2", ], meta.mem, cell.type = "CD8+ Memory")
df.plot <- rbind(df.plot.nai, df.plot.mem)
df.plot$group <- factor(df.plot$group, levels = c("HY", "HO"))
ggplot(df.plot , aes(group, expr)) +
geom_boxplot(aes(color = sex, shape = cell.type)) +
geom_point(aes(color = sex, shape = cell.type), size = 3.5) +
facet_wrap(~cell.type + sex , nrow = 1) +
ggtitle("") + ylim(c(3,8)) +
xlab("") + ylab("") +
theme_pubr(base_size = 12) +
theme(strip.text = element_blank()) +
theme(legend.position="right") +
stat_compare_means(comparisons = list(c("HO","HY")),
size = 4, method = "wilcox.test", paired = FALSE,
tip.length = 0,
vjust = 0, position = 8, label.y = 7.9) +
scale_color_manual(values = color_values) +
labs(color = "Sex", shape = "Cell Type")
ggsave("output/F4/DE_genes/Human_CD8_ATACseq_FOSL2.pdf",useDingbats = F, width = 6, height = 4)
```
```{r Common TFs p-values}
read_excel_allsheets <- function(filename, tibble = FALSE) {
# I prefer straight data.frames
# but if you like tidyverse tibbles (the default with read_excel)
# then just pass tibble = TRUE
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
human.tfs <- read_excel_allsheets("Supplementary/Summary Tables/Supplementary_Table_Motif_Human.xlsx")
# We are interested in opening peak related motif enrichments
pbmc.opening <- human.tfs$Opening_Human_PBMC
cd8.naive.opening <- human.tfs$Opening_Human_CD8_Naive
cd8.memory.opening <- human.tfs$Opening_Human_CD8_Memory
motif.table <- list(PBMC = pbmc.opening, `CD8+ Naive` = cd8.naive.opening, `CD8+ Memory` = cd8.memory.opening)
TFs <- c("NFE2L2","Fos", "Fosl2", "JunB", "JunD", "Jdp2")
logp.vals <- lapply(motif.table, function(x){
x.split <- strsplit(x$Motif.Name, "(", fixed = T) %>%
sapply(function(x){x[1]})
-1* x[match(TFs, x.split),"Log.P.value"]
}) %>% bind_rows(.id = "TCT") %>% as.data.frame
rownames(logp.vals) <- toupper(TFs)
stars <- ifelse(logp.vals > -log(0.001), "***",
ifelse(logp.vals > -log(0.01), "**",
ifelse(logp.vals > -log(0.05), "*", "")))
breaksList <- seq(-3, 3, by = .01)
pdf("output/F5/Heatmap_Human_Shared_TFs_Motif_Enrichment.pdf", useDingbats = F, width = 5, height = 3)
pheatmap(logp.vals[-6,],
color = grDevices::colorRampPalette((RColorBrewer::brewer.pal(n=7,"Blues")))(length(breaksList)),
cluster_cols = F, cluster_rows = F,
display_numbers = stars[-6,], fontsize_number = 14, number_color = "white", border_color = "white",
show_colnames = T, angle_col = 0)
dev.off()
```