Figure 6
Load cohort annotation file and Epithelial cells Seurat object
Figure 6-a
anno.class = anno.class.obs
############
class = "Endometrial-type Epithelium"
sel.sc = readRDS("rds/EnEpi_cells.rds")
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")
.sel.sc = subset(sel.sc, subset = SampleName %in% unique(anno.class$SampleName))
prop.cells <- data.frame(table(.sel.sc@meta.data$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$Var1)
ggplot(prop.cells, aes(Var1, Freq)) +
geom_col() +
theme_minimal(base_size = 15) +
geom_text(aes(label=Freq), position=position_dodge(width=0.9), vjust=-0.25) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) +
labs(y = "Number of Cells")
.sel.sc@meta.data$Surgical = NA
for (i in 1:nrow(anno.class)) {
sample = anno.class$SampleName[i]
.sel.sc@meta.data$Surgical[.sel.sc@meta.data$SampleName == sample] <- anno.class$CominedSurgical[i]
}
Idents(object = .sel.sc) <- .sel.sc@meta.data$Surgical
DEG.res <- FindAllMarkers(.sel.sc, test.use = "MAST")
fc.p = 0.9
pv.p = 0.05
DEG.res$STATUS = "NOT.SIG"
DEG.res[DEG.res$avg_log2FC < -fc.p & DEG.res$p_val_adj < pv.p, ]$STATUS = "Down"
DEG.res[DEG.res$avg_log2FC > fc.p & DEG.res$p_val_adj < pv.p, ]$STATUS = "Up"
feature = "Surgical"
c = "Deep"
markers.filt = DEG.res[DEG.res$cluster == c,]
nDEG = nrow(markers.filt)
genes = markers.filt$gene[markers.filt$cluster == c & markers.filt$STATUS != "NOT.SIG"]
pv <- ggplot(markers.filt, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
geom_point(aes(color = STATUS), cex = 1.45) +
scale_color_manual(values = c("darkgreen", "grey", "red")) +
ylim(0, 65) +
theme_bw(base_size = 15) +
theme(legend.position = "bottom",
plot.title = element_text(hjust = 0.5)) +
geom_text_repel(
data = subset(markers.filt, STATUS == "Up"),
aes(label = markers.filt$gene[which(markers.filt$gene %in% genes & markers.filt$STATUS %in% "Up")]),
segment.size = 0.4,
direction = "y",
segment.color = "grey50",
hjust = 0
) +
geom_text_repel(
data = subset(markers.filt, STATUS == "Down"),
aes(label = markers.filt$gene[which(markers.filt$gene %in% genes & markers.filt$STATUS %in% "Down")]),
segment.size = 0.4,
direction = "y",
segment.color = "grey50",
hjust = 1
) +
geom_hline(yintercept = -log10(pv.p), linetype="dotted", color = "black", size= 0.5) +
geom_vline(xintercept = -fc.p, linetype="dotted", color = "black", size= 0.5) +
geom_vline(xintercept = fc.p, linetype="dotted", color = "black", size= 0.5) +
labs(title = paste0(feature, " : ", c, " (n=", nDEG, ") FC: ", fc.p, " pval = ", pv.p))
pv
Figure 6-b
res <- compareCluster(gs.ephi, fun="enrichPathway")
dp + RotatedAxis()
Figure 6-c
class = "Endometrial-type Stroma"
sel.sc = readRDS("rds/EnS_cells.rds")
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")
.sel.sc = subset(sel.sc, subset = SampleName %in% unique(anno.class$SampleName))
prop.cells <- data.frame(table(.sel.sc@meta.data$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$Var1)
ggplot(prop.cells, aes(Var1, Freq)) +
geom_col() +
theme_minimal(base_size = 15) +
geom_text(aes(label=Freq), position=position_dodge(width=0.9), vjust=-0.25) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) +
labs(y = "Number of Cells")
.sel.sc@meta.data$Surgical = NA
for (i in 1:nrow(anno.class)) {
sample = anno.class$SampleName[i]
.sel.sc@meta.data$Surgical[.sel.sc@meta.data$SampleName == sample] <- anno.class$CominedSurgical[i]
}
Idents(object = .sel.sc) <- .sel.sc@meta.data$Surgical
DEG.res <- FindAllMarkers(.sel.sc, test.use = "MAST")
fc.p = 1.1
pv.p = 0.05
DEG.res$STATUS = "NOT.SIG"
DEG.res[DEG.res$avg_log2FC < -fc.p & DEG.res$p_val_adj < pv.p, ]$STATUS = "Down"
DEG.res[DEG.res$avg_log2FC > fc.p & DEG.res$p_val_adj < pv.p, ]$STATUS = "Up"
feature = "Surgical"
c = "Deep"
markers.filt = DEG.res[DEG.res$cluster == c,]
nDEG = nrow(markers.filt)
genes = markers.filt$gene[markers.filt$cluster == c & markers.filt$STATUS != "NOT.SIG"]
pv <- ggplot(markers.filt, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
geom_point(aes(color = STATUS), cex = 1.45) +
scale_color_manual(values = c("darkgreen", "grey", "red")) +
ylim(0, 65) +
theme_bw(base_size = 15) +
theme(legend.position = "bottom",
plot.title = element_text(hjust = 0.5)) +
geom_text_repel(
data = subset(markers.filt, STATUS == "Up"),
aes(label = markers.filt$gene[which(markers.filt$gene %in% genes & markers.filt$STATUS %in% "Up")]),
segment.size = 0.4,
direction = "y",
segment.color = "grey50",
hjust = 0
) +
geom_text_repel(
data = subset(markers.filt, STATUS == "Down"),
aes(label = markers.filt$gene[which(markers.filt$gene %in% genes & markers.filt$STATUS %in% "Down")]),
segment.size = 0.4,
direction = "y",
segment.color = "grey50",
hjust = 1
) +
geom_hline(yintercept = -log10(pv.p), linetype="dotted", color = "black", size= 0.5) +
geom_vline(xintercept = -fc.p, linetype="dotted", color = "black", size= 0.5) +
geom_vline(xintercept = fc.p, linetype="dotted", color = "black", size= 0.5) +
labs(title = paste0(feature, " : ", c, " (n=", nDEG, ") FC: ", fc.p, " pval = ", pv.p))
pv
Figure 6-d
res <- compareCluster(gs.ephi, fun="enrichPathway")
dp + RotatedAxis()