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4_TBCpeakProfiling-gd19.5.Rmd
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4_TBCpeakProfiling-gd19.5.Rmd
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---
title: "scATAC-seq Rat Metrial Glands"
author: "Ha T. H. Vu"
output: html_document
---
```{r setup, include=FALSE}
options(max.print = "75")
knitr::opts_chunk$set(
echo = TRUE,
collapse = TRUE,
comment = "#>",
fig.path = "Files/",
fig.width = 15,
prompt = FALSE,
tidy = FALSE,
message = FALSE,
warning = TRUE
)
knitr::opts_knit$set(width = 75)
```
This is a documentation for analyses of scATAC-seq data, generated from rat metrial gland tissues on gestational day (GD) 15.5 and 19.5. <br>
Here, focus on only TBC specific peaks.
# gd 19.5
```{r}
library(Signac)
library(Seurat)
library(ggplot2)
library(ggpubr)
library(GenomicRanges)
library(patchwork)
library(dplyr)
library(BSgenome.Rnorvegicus.Ensembl.rn6)
set.seed(1234)
load("/work/LAS/geetu-lab/hhvu/project3_scATAC/rnor6.rda")
load("/work/LAS/geetu-lab/hhvu/project3_scATAC/rnor6.ranges.rda")
load("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/scRNA-seqFiles/gd19.5-4-5-6-7_res0.8.rda")
exp <- AverageExpression(data2)
exp <- as.data.frame(exp$RNA)
avgRNA <- exp
load("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/2_MACS2Peaks/annotation_scRNAseq/gd19.5-ATACwithRNAlables.rda")
Idents(rats) <- [email protected]$predicted.id # change from old identities to those predicted by scRNA-seq
DefaultAssay(rats) <- 'MACSpeaks'
Annotation(rats) <- rnor6.ranges
avgATAC <- AverageExpression(rats, "MACSpeaks")
avgATAC <- avgATAC$MACSpeaks
```
1. Distribution of number of peaks
```{r}
specificPeaks <- read.table("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/2_MACS2Peaks/annotation_scRNAseq/gd19.5-tbc-toGenes.txt", sep = "\t")
t <- as.data.frame(table(specificPeaks$gene_name))
ggplot(t, aes(x = Freq)) + geom_histogram(bins = 7, fill = "#c6dbef", color="grey") +
scale_x_continuous(breaks=c(0:7)) +
stat_bin(aes(y=..count.., label=ifelse(..count..==0,"",..count..)), geom="text", vjust=-.5) +
theme_bw() +
ggtitle("Distribution of peak number per gene - gd 19.5")
```
2. Peaks with >= 3 peaks tend to be higher expressed:
```{r}
t <- table(specificPeaks$gene_name)
genes <- names(t[t >= 3])
exp <- exp[order(rownames(exp)),]
exp$gene <- rownames(exp)
df <- exp[exp$gene %in% names(t), c("gene", "6")]
colnames(df) <- c("gene", "value")
df2 <- data.frame(gene = setdiff(names(t), df$gene), value = rep(0, length(setdiff(names(t), df$gene))))
names(df2) <- colnames(df)
df <- rbind(df, df2)
df$category <- ifelse(df$gene %in% genes, ">= 3 peaks", "< 3 peaks")
wilcox.test(df[df$category == ">= 3 peaks", "value"], df[df$category == "< 3 peaks", "value"])
ggplot(df, aes(x=category, y=log10(value+10^(-5)))) +
geom_boxplot(fill = c("#c6dbef", "#08519c")) +
ggtitle("Cutoff = 3, TBC peaks all genes GD19.5") +
geom_signif(stat="identity",
data=data.frame(x=c(1), xend=c(2),
y=c(2.8), annotation=c("*")),
aes(x=x, xend=xend, y=y, yend=y, annotation=annotation),
tip_length = 2) +theme_bw()
#Number of genes with >= 3 peaks
length(genes)
#Number of genes with < 3 peaks
length(unique(df[df$category == "< 3 peaks", "gene"]))
```
3. How many markers from scRNA-seq are there in each gene group
```{r}
marker <- read.table("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/scRNA-seqFiles/gd19.5-wilcoxin-cluster6.txt", header = T)
#Number of genes with >= 3 peaks and are also markers
length(intersect(genes, marker$gene))
#Percent genes with >= 3 peaks and are also markers
print(paste0((length(intersect(genes, marker$gene))/length(genes))*100, "%"))
#Number of genes with < 3 peaks and are also markers
length(intersect(unique(df[df$category == "< 3 peaks", "gene"]), marker$gene))
#Percent of genes with < 3 peaks and are also markers
print(paste0((length(intersect(unique(df[df$category == "< 3 peaks", "gene"]), marker$gene))/length(unique(df[df$category == "< 3 peaks", "gene"])))*100, "%"))
```
Test if this is significant
```{r}
q <- length(intersect(genes, marker$gene)) - 1 #number of markers found
m <- length(marker$gene) #number of successes aka markers
n <- length(specificPeaks$gene_name) - m #number of failures, ie number of genes that are not markers
k <- length(genes) #number of draws, ie number of genes with >= 3 peaks
phyper(q, m, n, k, lower.tail = FALSE, log.p = FALSE)
```
5. Plotting peaks around genes
```{r}
library(ensembldb)
library(biovizBase)
library(EnsDb.Rnorvegicus.v98)
library(AnnotationHub)
library(AnnotationForge)
load("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/2_MACS2Peaks/annotation_scRNAseq/gd19.5-ATACwithRNAlables.rda")
Idents(rats) <- [email protected]$predicted.id # change from old identities to those predicted by scRNA-seq
DefaultAssay(rats) <- 'MACSpeaks'
annotations <- suppressWarnings(GetGRangesFromEnsDb(ensdb = EnsDb.Rnorvegicus.v98))
annotations <- subset(annotations, gene_biotype=="protein_coding")
seqlevelsStyle(annotations) <- "Ensembl"
Annotation(rats) <- annotations
Idents(rats) <- factor(Idents(rats), levels = c("Other cells", "Natural killer cells", "Endothelial cells", "Macrophage cells", "Smooth muscle cells", "Invasive trophoblast cells"))
cols <- c("orange3", "#718748", "#183D61", "#79704C", "red3")
cov_plot <- CoveragePlot(
object = rats,
region = "17-38323674-38330944",
feature = "Prl5a1",
annotation = T,
peaks = TRUE,
heights = c(6, 2, 2),
extend.upstream = 300000,
extend.downstream = 32000,
idents = c("Natural killer cells", "Endothelial cells", "Macrophage cells",
"Smooth muscle cells", "Invasive trophoblast cells")
)
wrap_elements(cov_plot & scale_fill_manual(values = cols) & xlab("Chromosome 17 position")) + ggtitle("GD19.5 Prl5a1") + theme(plot.title = element_text(size=18, face = "bold"))
```
6. Genes in EVT
```{r}
library(stringr)
geneToPeak <- read.table("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/5_integrating2timepoints/annotation_scRNAseq/commonPeaks/EVT_GREAT/EVT_genesToPeaks.txt", header = F, sep = "\t", fill = T, stringsAsFactors = F, quote = "")
geneToPeak$noPeaks <- str_count(geneToPeak$V2, "EVT_peaks")
quantile(geneToPeak$noPeaks)
```
```{r}
closest_genes <- read.table("/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/4_RNAintegration/2_MACS2Peaks/annotation_scRNAseq/gd19.5-tbc-toGenes.txt", sep = "\t")
closest_genes$gene_name <- toupper(closest_genes$gene_name)
load(file = "/work/LAS/geetu-lab/hhvu/combine-test-expression1.Rdata")
ratHumanOrthologs <- dataset$GRCH38$ratHumanOrthologs
closest_genes2 <- inner_join(closest_genes, ratHumanOrthologs[,c(2,4)], by = c("gene_name" = "Gene.name"))
t <- table(closest_genes2$Human.gene.name)
t <- as.data.frame(t)
t$Var1 <- toupper(t$Var1)
geneToPeak3 <- geneToPeak[geneToPeak$V1 %in% closest_genes2$Human.gene.name,]
geneToPeak3 <- inner_join(geneToPeak3, t, by = c("V1" = "Var1"))
geneToPeak3$category <- ifelse(geneToPeak3$Freq >= 3, ">= 3 rats", "< 3 rats")
wilcox.test(geneToPeak3[geneToPeak3$category == ">= 3 rats", "noPeaks"], geneToPeak3[geneToPeak3$category == "< 3 rats", "noPeaks"])
ggplot(geneToPeak3, aes(x=category, y=noPeaks)) +
geom_boxplot(fill = c("#c6dbef", "#08519c")) +
ggtitle("Cutoff = 3, invasive trophoblast GD19.5") +
geom_signif(stat="identity",
data=data.frame(x=c(1), xend=c(2),
y=c(120), annotation=c("*")),
aes(x=x, xend=xend, y=y, yend=y, annotation=annotation),
tip_length = 2) +theme_bw()
s <- geneToPeak3[,1:4]
colnames(s) <- c("humanGene", "EVTpeaks", "noEVTpeaks", "noRatPeaks")
#write.table(s, "/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/5_integrating2timepoints/annotation_scRNAseq/commonPeaks/EVT_GREAT/gd19.5EVTgenesWithRatPeaks.txt", sep = "\t", quote = F, row.names = F)
```
```{r}
t <- table(specificPeaks$gene_name)
t2 <- exp[intersect(names(t), rownames(exp)), c("6", "gene")]
temp <- data.frame("6"=rep(0, length(setdiff(names(t), rownames(exp)))),
gene=setdiff(names(t), rownames(exp)))
names(temp) <- c("6", "gene")
t2 <- rbind(t2, temp)
t2$marker <- ifelse(t2$gene %in% marker$gene, "1", "0")
t2 <- inner_join(t2, specificPeaks[,1:2], by = c("gene" = "gene_name"))
t2$capGene <- toupper(t2$gene)
t2 <- left_join(t2, ratHumanOrthologs, by = c("capGene" = "Gene.name"))
t2 <- left_join(t2, s, by = c("Human.gene.name" = "humanGene"))
#write.table(t2, "/work/LAS/geetu-lab/hhvu/project3_scATAC/scATAC-seq-analysis/5_integrating2timepoints/annotation_scRNAseq/commonPeaks/gd19.5-tableS2.txt", sep = "\t", quote = F, row.names = F)
#cut -f 1-4,8,10 gd19.5-tableS2.txt | uniq -c > gd19.5-tableS2.2.txt
```
Candidate marker
```{r}
distinct(t2[t2$`6` > 1 & t2$marker == 0 & t2$noEVTpeaks >= 3 & t2$noRatPeaks >= 3 & !is.na(t2$Human.gene.name), c(1,2,3,4,8,10)])
```
```{r}
sessionInfo()
```