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---
title: "Figure 4"
author: Tobias Roider
date: "Last compiled on `r format(Sys.time(), '%d %B, %Y, %X')`"
output:
rmdformats::readthedown:
self_contained: false
editor_options:
chunk_output_type: console
---
```{r options, include=FALSE, warning = FALSE}
library(knitr)
opts_chunk$set(echo=TRUE, tidy=FALSE, include=TRUE, message=FALSE,
dpi = 100, cache = FALSE, warning = FALSE)
opts_knit$set(root.dir = "../")
options(bitmapType = "cairo")
```
# Read data, functions and packages
```{r read data}
source("R/ReadPackages.R")
source("R/Functions.R")
source("R/ReadData.R")
source("R/ThemesColors.R")
source("R/Helpers.R")
```
# Clonotypes projected onto UMAP
```{r clonotype sizes, fig.height=2.5}
set.seed(1)
df_clonotypes <-
DFtotal_5prime %>%
filter(!is.na(raw_clonotype_id)) %>%
select(Barcode_full, PatientID, refUMAP_1, refUMAP_2, Entity, IdentI, raw_clonotype_id) %>%
distinct() %>%
add_count(IdentI, PatientID, raw_clonotype_id) %>% ungroup() %>%
select(-Barcode_full) %>%
group_by(PatientID, raw_clonotype_id, IdentI) %>%
summarise(refUMAP_1=median(refUMAP_1), refUMAP_2=median(refUMAP_2), n, Entity, .groups = "drop") %>%
distinct() %>%
#mutate(n=ifelse(n>50, 50, n)) %>%
mutate(Entity=factor(Entity, levels=c("rLN", "DLBCL", "MCL", "FL", "MZL")))
DF_5prime_umap <- DFtotal_5prime %>%
select(refUMAP_1, refUMAP_2, IdentI, Entity, PatientID) %>%
distinct() %>%
mutate(Entity=factor(Entity, levels=c("rLN", "DLBCL", "MCL", "FL", "MZL")))
df_clonotypes_subset <- list()
# Filter clonotypes to avoud overplotting
w <- 30
#for(e in entities){
for(i in c(3,5,6)){
for(p in unique(df_clonotypes$PatientID)){
tmp <- df_clonotypes %>%
filter(n>2, IdentI==i, PatientID==p)
if(nrow(tmp)>w){
df_clonotypes_subset[[paste0(i, p)]] <- tmp %>% sample_n(size = w)
} else {
df_clonotypes_subset[[paste0(i, p)]] <- tmp
}
}
}
# }
rm(w,p)
df_clonotypes_subset <- bind_rows(df_clonotypes_subset) %>%
rbind(df_clonotypes %>% filter(n>2, !IdentI %in% c(3,5,6)),
.)
p1 <- ggplot()+
geom_point_rast(data=DF_5prime_umap %>% select(-Entity), aes(x=refUMAP_1, y=refUMAP_2),
size=0.2, alpha=0.2, stroke=0, shape=21, fill="grey90")+
geom_point_rast(data=DF_5prime_umap, aes(x=refUMAP_1, y=refUMAP_2, fill=IdentI), size=0.2,
alpha=ifelse(DF_5prime_umap$IdentI %in% c("6", "3", "5", "11", "14"), 0.04, 0.2),
stroke=0, shape=21)+
geom_point(data=df_clonotypes_subset, aes(x=refUMAP_1, y=refUMAP_2, size=n, color=IdentI), shape=21, stroke=0.25,
alpha=ifelse(df_clonotypes_subset$Entity %in% c("FL", "MZL"), 1, 0.75))+
scale_color_manual(values = colors_umap_cl, guide="none")+
scale_fill_manual(values = colors_umap_cl, guide="none")+
scale_size_continuous(range=c(1, 5), limits=c(3, 50), breaks=c(3, 20, 35, 50),
labels=c("3", "20", "35", "> 50"), name = NULL)+
facet_wrap(~Entity, nrow = 1)+
geom_text(data = df_clonotypes %>% select(PatientID, Entity) %>% distinct() %>% add_count(Entity), aes(label = paste0("n = ", n)),
x = 9.65, y = -6.9, hjust=1, check_overlap = T, size=2.5)+
xlab("refUMAP-1")+
ylab("refUMAP-2")+
mytheme_1+
theme(legend.position = "top",
legend.text = element_text(size=7),
legend.background = element_rect(fill=NA),
legend.box = unit("cm", x = 0.01),
legend.spacing.x = unit("cm", x = 0.05),
legend.spacing.y = unit("cm", x = 0.001),
panel.border = element_rect(size=0.25, color="black"),
legend.box.spacing = unit(0, units = "cm"),
strip.background = element_rect(size=0.25),
legend.box.margin = unit(c(0,-12.5,-0.1,0), units = "cm"))+
labs(tag = "A")
p1
```
# Quantification of clonotypes
```{r quantify clonotypes, fig.height=2.5}
df_clon_sign <- df_clonotypes %>% mutate(isClonal=ifelse(n>1, "clonal", "not")) %>%
group_by(isClonal, PatientID, IdentI) %>%
summarise(Total=sum(n)) %>%
fill_zeros(names_from = "IdentI", values_from = "Total") %>%
pivot_wider(names_from = "isClonal", values_from = "Total") %>%
mutate(Prop=round(clonal/(not+clonal), 2)) %>%
add_entity() %>%
filter(IdentI %in% c("5", "3", "6", "11")) %>%
mutate(Entity=factor(Entity, levels = c("rLN", "DLBCL", "MCL", "FL", "MZL"))) %>%
mutate(Prop=ifelse(is.nan(Prop), 0, Prop)) %>%
ungroup() %>%
mutate(IdentI=factor(IdentI, levels=cluster_order)) %>%
mutate(Label=factor(IdentI, levels=cluster_order, labels = labels_cl_parsed[as.character(cluster_order)]))
df_clon_sign_p <-
df_clon_sign %>%
mutate(Entity=as.factor(Entity)) %>%
mutate(Prop=Prop+sample(seq(0.01, 0.03, 0.0001), 17)) %>%
group_by(IdentI) %>%
wilcox_test(data=., formula = Prop ~ Entity, ref.group = "rLN", alternative = "less") %>%
select(IdentI, Entity=group2, p) %>%
mutate(Entity=factor(Entity, levels = c("rLN", "DLBCL", "MCL", "FL", "MZL"))) %>%
mutate(p=ifelse(p>0.05, NA, p)) %>%
filter(!is.na(p)) %>%
left_join(., data.frame(IdentI=factor(c(5,6,11)), height=c(106, 62.5, 50)))
p <- list()
for(i in c(1:4)){
y <- list(c(3),c(5),c(6),c(11))[[i]]
ylim <- c(65, 125, 75, 58)
p[[i]] <- df_clon_sign %>%
filter(IdentI %in% y) %>%
ggplot(aes(x=Entity, y=100*Prop, fill=IdentI))+
ggbeeswarm::geom_beeswarm(cex = 3.5, stroke=0.25, groupOnX = TRUE, shape = 21, size = 1.25, alpha = 1, color="white")+
#geom_text(data=df_clon_sign_p %>% filter(IdentI %in% y), inherit.aes = F, aes(y=height, x=Entity, label=p), hjust=0.1, size=2.25, angle=45)+
geom_text(data=df_clon_sign_p %>% filter(IdentI %in% y), inherit.aes = F, aes(y=height, x=Entity, label=p), hjust=0.2, size=2.5, angle=45)+
scale_color_manual(values = colors_umap_cl, guide="none")+
scale_fill_manual(values = colors_umap_cl, guide="none")+
ylab("Cell number")+
scale_y_continuous(name="% of clontypes of size > 1", limits=c(0,ylim[i]))+
scale_x_discrete(expand = c(0.17,0.17))+
facet_wrap(~Label, ncol = 2, labeller = label_parsed)+
mytheme_1+
theme(axis.title.x = element_blank(),
strip.background = element_rect(color=NA),
plot.margin = unit(c(0.1, 0.2, 0, 0), units = "cm"),
panel.border = element_rect(size = 0.5),
axis.text.x = element_text(angle=45, hjust = 1))
if(i!=1){
p[[i]] <- p[[i]]+
theme(axis.title.y = element_blank()
)
}
if(i==1){
p[[i]] <- p[[i]]+
labs(tag = "B")
}
if(i!=4){
p[[i]] <- p[[i]]+theme(
plot.margin = unit(c(0.1, 0.5, 0, 0), units = "cm"))
}
}
wrap_plots(p, nrow = 1)
#ggsave(width = 16, height = 5, units = "cm", filename = "ResponseFigure4.pdf")
```
# Shared clonotypes
```{r shared clonotypes, fig.height=2.3}
df_clonotypes_shared <-
left_join(DFtotal_5prime %>% filter(!is.na(raw_clonotype_id)) %>%
select(Barcode_fulla=Barcode_full, PatientID, refUMAP_1a=refUMAP_1, refUMAP_2a=refUMAP_2, IdentIa=IdentI, raw_clonotype_id) %>% distinct(),
DFtotal_5prime %>% filter(!is.na(raw_clonotype_id)) %>%
select(Barcode_fullb=Barcode_full, PatientID, refUMAP_1b=refUMAP_1, refUMAP_2b=refUMAP_2, IdentIb=IdentI, raw_clonotype_id) %>% distinct()
) %>%
filter(Barcode_fulla!=Barcode_fullb) %>%
filter(IdentIa!=IdentIb)
df_clonotypes_shared_subset <-
df_clonotypes_shared %>%
add_entity() %>%
mutate(Entity=factor(Entity, levels=c("rLN", "DLBCL", "MCL", "FL", "MZL"))) %>%
filter(PatientID %in% c("LN0132", "LN0302", "LN0193", "LN0198", "LN0078")) %>%
filter(IdentIa==14) %>%
mutate(Ident_shared=ifelse(IdentIa==14, IdentIb, IdentIa))
p2 <- ggplot()+
geom_point_rast(data=DF_5prime_umap %>% select(-Entity), aes(x=refUMAP_1, y=refUMAP_2),
size=0.2, alpha=0.2, stroke=0, shape=21, fill="grey90")+
geom_point_rast(data=DF_5prime_umap %>%
filter(PatientID %in% c("LN0132", "LN0302", "LN0193", "LN0198", "LN0078")) %>%
mutate(PatientID_new=paste0(PatientID, " (", Entity, ")")),
aes(x=refUMAP_1, y=refUMAP_2, fill=IdentI), size=0.25, alpha=0.5, stroke=0, shape=21)+
geom_curve(data= df_clonotypes_shared_subset,
aes(x=refUMAP_1a, y=refUMAP_2a, xend=refUMAP_1b, yend=refUMAP_2b, color=Ident_shared,
group=paste(raw_clonotype_id, PatientID)), size=0.15, alpha=0.2, curvature = 0.3)+
scale_fill_manual(values = colors_umap_cl, guide="none")+
scale_color_manual(values = colors_umap_cl, guide="none")+
facet_wrap(~Entity, nrow = 1)+
geom_text(data = data.frame(PatientID=c("LN0132", "LN0302", "LN0193", "LN0198", "LN0078")) %>%
add_entity() %>%
mutate(Entity=factor(Entity, levels=c("rLN", "DLBCL", "MCL", "FL", "MZL"))), aes(label = PatientID),
x = 9.65, y = -6.9, hjust=1, check_overlap = T, size=2.5)+
xlab("refUMAP-1")+
ylab("refUMAP-2")+
ggtitle(expression('Pairs of identical clonotypes between T'[Pr]~'and other T-cell subsets'))+
mytheme_1+
theme(legend.position = "none",
strip.background = element_rect(size=0.25),
panel.border = element_rect(size=0.25, color="black"))+
labs(tag = "C")
p2
```
# Additional legend
```{r legend, fig.height=1.5}
p_legend <-
df_comb %>%
sample_n(100) %>%
ggplot(aes(x=wnnUMAP_1, y=wnnUMAP_2, color=as.factor(IdentI), fill=as.factor(IdentI)))+
geom_point(size=2.25, stroke=0, shape=21, alpha=1)+
scale_color_manual(values = colors_umap_cl, limits=factor(cluster_order), labels=unlist(labels_cl))+
scale_fill_manual(values = colors_umap_cl, limits=factor(cluster_order), labels=unlist(labels_cl))+
guides(fill=guide_legend(nrow = 2, byrow = T))+
guides(color=guide_legend(nrow = 2, byrow = T))+
coord_fixed(clip = "off")+
mytheme_1+
theme_void()+
theme(legend.position = "top",
legend.text = element_text(size=7.2, margin = unit(units = "cm", x = c(0,0,0,-0.3))),
legend.spacing.x = unit("cm", x = 0.42),
legend.spacing.y = unit("cm", x = 0.001),
legend.key.width = unit("cm", x = 0.055),
legend.key.height = unit("cm", x = 0.5),
legend.box.spacing = unit(0, units = "cm"),
legend.title = element_blank())
as_ggplot(get_legend(p_legend))
#ggsave(as_ggplot(get_legend(p_legend)), width = 13, height = 1, units = "cm", filename = "Figure4_legend.pdf")
```
# Compose
```{r}
p_full <- p1/wrap_plots(p[[1]], p[[2]], p[[3]], p[[4]], nrow = 1)/p2
#ggsave(p_full, width = 18.5, height = 16.75, units = "cm", filename = "Figure4.pdf")
```
# Session info
```{r session info}
sessionInfo()
```