---

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()

```