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figure5.Rmd
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figure5.Rmd
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---
title: "Figure 5"
knit: (function(inputFile, encoding) {
rmarkdown::render(inputFile, encoding = encoding, output_dir = "docs") })
---
```{r include = FALSE}
rm(list = ls())
library(ComplexHeatmap)
library(circlize)
library(Seurat)
library(ggplot2)
library(ggrepel)
library(dplyr)
library(plotly)
library(cowplot)
library(tidyr)
library(corrplot)
library('org.Hs.eg.db')
library(ReactomePA)
library("AnnotationDbi")
library("clusterProfiler")
library(knitr)
library(RColorBrewer)
library (pheatmap)
```
Load cohort annotation file and Mesenchymal cells Seurat object
## Figure 5-a
```{r include = T, eval=T, fig.align='center', message=F, warning=F}
.sc.fibro = readRDS(file = "rds/fibroblasts.annotated.rds")
.sc.fibro@meta.data$Major.Class[.sc.fibro@meta.data$Major.Class == "Extra-ovarian endometriosis"] = "Endometriosis"
p4 = DimPlot(object = .sc.fibro, pt.size = 0.1, group.by ="active.cluster", label = T, raster=FALSE)
p4
```
```{r include = T, eval=T, fig.align='center', message=F, warning=F, fig.width=12, fig.height=4}
p4 = DimPlot(object = .sc.fibro, pt.size = 0.1, group.by ="active.cluster", split.by = "Major.Class", label = F, raster=FALSE, ncol = 5)
p4
.sc.fibro@active.ident <- factor(.sc.fibro@active.ident,
levels=rev(c("EnS_Proliferative",
"EnS_Secretory",
"Fibro (1)",
"Fibro (2)",
"Fibro (3)",
"Fibro (4)",
"C7_Fibro (1)",
"C7_Fibro (2)",
"C7_Fibro (3)",
"Activated_Fibro",
"C7_Activated_Fibro",
"Smooth_Muscle",
"GAS5+"))
)
levels(.sc.fibro@meta.data$active.cluster) <-rev(c("EnS_Proliferative",
"EnS_Secretory",
"Fibro (1)",
"Fibro (2)",
"Fibro (3)",
"Fibro (4)",
"C7_Fibro (1)",
"C7_Fibro (2)",
"C7_Fibro (3)",
"Activated_Fibro",
"C7_Activated_Fibro",
"Smooth_Muscle",
"GAS5+"))
.sc.fibro@meta.data$active.cluster <- .sc.fibro@active.ident
```
## Figure 5-b
``` {r include=T, eval=F, message=F, warning=F, fig.align='center'}
table.gene.markers <- data.frame(Gene=c("MME"),
Cell="Endometrial-type stroma")
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("ESR1", "PGR", "IGF1", "MMP11", "CRABP2", "ECM1"),
Cell="Proliferative"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("PAEP", "FOXO1", "IL15"),
Cell="Secretory"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("DCN", "PDGFRA", "THY1", "COL1A1", "PCOLCE"),
Cell="Fibroblasts"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("C7", "OGN", "CFD"),
Cell="C7 fibroblasts"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("ITGB1", "FAP", "ACTA2"),
Cell="Activated fibroblasts"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("ACTG2", "MCAM"),
Cell="Uterine smooth muscle"))
table.gene.markers <- rbind(table.gene.markers,
data.frame(Gene=c("GAS5"),
Cell="GAS5+"))
pd <- DotPlot(object = .sc.fibro, assay="RNA", features = unique(table.gene.markers$Gene)) +
theme(axis.text.x = element_text(angle = 90)) +
scale_colour_gradient2(low = "#2166ac", mid = "#f7f7f7", high = "#b2182b")
pd
```
``` {r include=T, message=F, eval=T, warning=F, fig.align='center', fig.width = 12, fig.height=8}
img1_path = "plots/6E-DOTPLOT-markers.pdf"
include_graphics(img1_path)
```
## Figure 5-c
``` {r include=T, message=F, warning=F, fig.align='center'}
bsize = 14
counts.prop = data.frame(table(.sc.fibro@meta.data$Major.Class, .sc.fibro@meta.data$active.cluster))
counts.prop.perc = group_by(counts.prop, Var2) %>% mutate(percent = Freq/sum(Freq))
aux = aggregate(counts.prop$Freq, by=list(Category=counts.prop$Var1), FUN=sum)
aux$Var2 = "Total"
colnames(aux) <- c("Var1", "Freq", "Var2")
aux$percent = aux$Freq / sum(aux$Freq)
counts.prop.perc = rbind(as.data.frame(counts.prop.perc), aux)
p1 <- ggplot(counts.prop.perc, aes(x = Var2, y = percent, fill = Var1, group=Var2)) +
geom_bar(position="stack",stat = "identity", width=0.8) +
scale_fill_manual(name="Class", values = c("Endometrioma" = "#7b3294",
"Eutopic Endometrium" = "#c2a5cf",
"Endometriosis" = "#d9f0d3",
"No endometriosis detected" = "#a6dba0",
"Unaffected ovary" = "#008837")) +
coord_flip() +
theme_set(theme_gray(base_size = bsize)) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 270, hjust = 1),
axis.text.y = element_text(angle = 0, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none")
p1
```
## Figure 5-d
``` {r include=T, eval=T, message=F, warning=F, fig.align='center'}
sel.sc = readRDS("rds/EnS_cells.rds")
sel.sc = subset(sel.sc, subset = Major.Class %in% c("Endometrioma",
"Eutopic Endometrium",
"Endometriosis"))
```
``` {r include=T, eval=F, message=F, warning=F, fig.align='center'}
.sc = subset(.sc.fibro, subset = active.cluster %in% c("EnS_Proliferative", "EnS_Secretory"))
sel.sc = subset(.sc, subset = Major.Class %in% c("Endometrioma",
"Eutopic Endometrium",
"Endometriosis"))
sel.sc@meta.data <- droplevels(sel.sc@meta.data)
```
``` {r include=T, message=F, warning=F, fig.align='center'}
prop.cells <- data.frame(table(sel.sc@meta.data$active.cluster))
prop.cells$Var1 <- factor(prop.cells$Var1, levels = prop.cells$Var1)
ggplot(prop.cells, aes(Var1, Freq)) +
geom_col() +
theme_minimal(base_size = 15) +
geom_text(aes(label=Freq), position=position_dodge(width=0.9), vjust=-0.25) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) +
labs(y = "Number of Cells")
prop.cells <- data.frame(table(sel.sc@meta.data$Major.Class))
prop.cells$Var1 <- factor(prop.cells$Var1, levels = prop.cells$Var1)
ggplot(prop.cells, aes(Var1, Freq)) +
geom_col() +
theme_minimal(base_size = 15) +
geom_text(aes(label=Freq), position=position_dodge(width=0.9), vjust=-0.25) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) +
labs(y = "Number of Cells")
```
``` {r include=T, message=F, warning=F, fig.align='center', fig.width = 5, fig.height=9}
Idents(object = sel.sc) <- sel.sc@meta.data$Major.Class
markers.sel.sc = FindAllMarkers(sel.sc, test.use = "MAST", verbose = TRUE)
p.v = 0.05
fc = 0.5
markers.sel.sc$STATUS = "NOT.SIG"
markers.sel.sc[markers.sel.sc$avg_log2FC < -fc & markers.sel.sc$p_val_adj < p.v, ]$STATUS = "Down"
markers.sel.sc[markers.sel.sc$avg_log2FC > fc & markers.sel.sc$p_val_adj < p.v, ]$STATUS = "Up"
features.deg = NULL
for (o in unique(markers.sel.sc$cluster)) {
features.deg = c(features.deg, head(markers.sel.sc$gene[markers.sel.sc$cluster == o & markers.sel.sc$STATUS == "Up"], 10))
}
pd <- DotPlot(sel.sc, features = unique(features.deg)) +
scale_colour_gradient2(low = "#2166ac", mid = "#f7f7f7", high = "#b2182b") +
coord_flip() +
RotatedAxis()
pd
```
## Figure 5-e
```{r include=T, warning=F, message=F, fig.align='center'}
info.table = data.frame(Patient=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21),
Endo=c("Yes", "Yes", "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"),
Menstrual.Cycle = c("Luteal", "Luteal", "Hormone", "Luteal", "Luteal", "Hormone", "Hormone", "Follicular", "Follicular", "Follicular",
"Luteal", "Hormone", "Hormone", "Luteal", "Luteal", "Follicular", "Luteal", "Hormone", "Luteal", "Follicular", "Follicular"),
Hormone=c("No", "No", "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "Yes", "No", "No", "No")
)
grid.newpage()
gridExtra::grid.table(info.table)
aux.seurat = readRDS("rds/aux.seurat.rds")
aux.seurat@meta.data$id.cells = rownames(aux.seurat@meta.data)
aux.seurat@meta.data$selected.cells = "No"
aux.seurat@meta.data$subcluster.name = NA
.sc.epi = readRDS(file = "rds/epithelial.annotated.rds")
table(.sc.epi@meta.data$active.cluster)
id.cells.annot = subset(.sc.epi, subset = active.cluster %in% c("KRT10/ACTA2 (1)", "KRT10/ACTA2 (2)", "KRT10/ACTA2 (3)"))$active.cluster
aux.seurat@meta.data$selected.cells[match(names(id.cells.annot), aux.seurat@meta.data$id.cells)] = "Yes"
aux.seurat@meta.data$subcluster.name[match(names(id.cells.annot), aux.seurat@meta.data$id.cells)] = as.character(id.cells.annot)
id.cells.annot = .sc.fibro@meta.data$id.cells[.sc.fibro@meta.data$active.cluster %in% c('EnS_Proliferative','EnS_Secretory')]
aux.seurat@meta.data$selected.cells[aux.seurat@meta.data$id.cells %in% id.cells.annot] = "Yes"
aux.seurat@meta.data$subcluster.name[match(rownames(.sc.fibro@meta.data)[.sc.fibro@meta.data$active.cluster %in% c('EnS_Proliferative','EnS_Secretory')],
aux.seurat@meta.data$id.cells)] = as.character(.sc.fibro@meta.data$active.cluster)[.sc.fibro@meta.data$active.cluster
%in% c('EnS_Proliferative','EnS_Secretory')]
sc.selected = subset(aux.seurat, subset = selected.cells == "Yes")
sc.selected = subset(sc.selected, subset = Major.Class %in% c("Endometrioma",
"Eutopic Endometrium",
"Endometriosis"))
info.table = info.table[!info.table$Menstrual.Cycle == "Hormone",]
sc.selected@meta.data$Endo = NA
sc.selected@meta.data$Menstrual.Cycle = NA
sc.selected@meta.data$Hormone = NA
for (i in 1:nrow(info.table)) {
p = info.table$Patient[i]
sc.selected@meta.data$Endo[sc.selected@meta.data$Patient.No. == p] <- info.table$Endo[i]
sc.selected@meta.data$Menstrual.Cycle[sc.selected@meta.data$Patient.No. == p] <- info.table$Menstrual.Cycle[i]
sc.selected@meta.data$Hormone[sc.selected@meta.data$Patient.No. == p] <- info.table$Hormone[i]
}
```
## Figure 5-f
```{r include=T, warning=F, message=F, fig.align='center', fig.width = 8, fig.height= 4}
.sel.sc = subset(sc.selected, subset = Major.Class %in% c("Eutopic Endometrium"))
prop <- data.frame(table(.sel.sc@meta.data$subcluster.name, .sel.sc@meta.data$Menstrual.Cycle))
counts.prop.perc = group_by(prop, Var1) %>% mutate(percent = Freq/sum(Freq))
aux = aggregate(prop$Freq, by=list(Category=prop$Var2), FUN=sum)
aux$Var1 = "Total"
colnames(aux) <- c("Var2", "Freq", "Var1")
aux$percent = aux$Freq / sum(aux$Freq)
counts.prop.perc = rbind(as.data.frame(counts.prop.perc), aux)
p1 <- ggplot(counts.prop.perc, aes(x = Var1, y = percent, fill = Var2, label = signif(round(percent, digits = 3), digits = 3))) +
geom_bar(position="stack",stat = "identity", width=0.8) +
scale_fill_manual(name="Menstrual cycle", values = c("Follicular" = "orange",
"Luteal" = "red",
"Hormone" = "blue")) +
geom_text(position = position_stack(vjust = 0.5), size = 4, color = "#ffffff") +
theme_set(theme_gray(base_size = bsize)) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(angle = 0, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "right")+
coord_flip()
p1
```
## Figure 5-g
```{r include=T, warning=F, message=F, fig.align='center'}
.sel.sc = subset(sc.selected, subset = Major.Class %in% c("Endometrioma",
"Endometriosis"))
prop <- data.frame(table(.sel.sc@meta.data$subcluster.name, .sel.sc@meta.data$Menstrual.Cycle))
counts.prop.perc = group_by(prop, Var1) %>% mutate(percent = Freq/sum(Freq))
aux = aggregate(prop$Freq, by=list(Category=prop$Var2), FUN=sum)
aux$Var1 = "Total"
colnames(aux) <- c("Var2", "Freq", "Var1")
aux$percent = aux$Freq / sum(aux$Freq)
counts.prop.perc = rbind(as.data.frame(counts.prop.perc), aux)
p1 <- ggplot(counts.prop.perc, aes(x = Var1, y = percent, fill = Var2, label = signif(round(percent, digits = 3), digits = 3))) +
geom_bar(position="stack",stat = "identity", width=0.8) +
scale_fill_manual(name="Menstrual cycle", values = c("Follicular" = "orange",
"Luteal" = "red",
"Hormone" = "blue")) +
geom_text(position = position_stack(vjust = 0.5), size = 4, color = "#ffffff") +
theme_set(theme_gray(base_size = bsize)) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(angle = 0, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "right")+
coord_flip()
p1
```