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---
title: "Figure 2"
author: Tobias Roider
date: "Last compiled on `r format(Sys.time(), '%d %B, %Y, %X')`"
output:
rmdformats::readthedown:
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")
```
# Most important markers
```{r most important markers, fig.height=2}
varImp(GBresults_surfaceplus, scale = TRUE)[[1]]
plot_markers <- varImp(GBresults_surfaceplus, scale = TRUE)[[1]] %>%
data.frame %>%
rownames_to_column("Parameter") %>%
top_n(n = 35, Overall) %>%
mutate(Parameter=gsub(Parameter, pattern = ".", fixed = T, replacement = "")) %>%
mutate(Parameter=gsub(Parameter, pattern = "tfactivity_|-E|integratedadt_|integratedrna_", replacement = "")) %>%
mutate(Parameter=ifelse(Parameter=="FOXP3", "FoxP3", Parameter)) %>%
mutate(Code=ifelse(Parameter %in% c("CD279", "CD4", "MKI67", "CD244", "FoxP3", "CD25",
"CD31", "CD45RA", "CD69", "CD185", "CD366", "IKZF3",
"CD45RO", "CD278", "CD8a"), 1, 0)) %>%
ggplot(aes(x=0, y=reorder(Parameter, -Overall)))+
geom_segment(aes(xend=Overall, yend=reorder(Parameter, Overall)), size=0.35)+
geom_point(aes(x=Overall, color=as.character(Code), y=reorder(Parameter, Overall)), inherit.aes = F, size=1)+
scale_color_manual(values = c("grey65", "#c51b8a")) +
scale_x_continuous(expand = c(0,0.25), limits = c(0,110), name = "Scaled importance")+
ggtitle("Top 35 features")+
coord_flip()+
mytheme_1+
theme(axis.title.x = element_blank(),
plot.title =element_text(hjust=0.5, face = "plain"),
panel.border = element_rect(size=0.25),
axis.ticks.y = element_line(size=0.25),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size=7),
axis.title.y = element_text(size=7),
axis.text.x = element_text(size=7, angle=45, hjust=1))+
labs(tag = "A")
plot_markers
#ggsave(width = 14, height = 4.25, units = "cm", filename = "Figure2_p1.pdf")
```
# Correlation: Flow cytometry ~ CITE-seq
```{r correlation}
df_cor <- left_join(df_freq %>% filter(!Population %in% c(df_comb$IdentI)),
df_facs, by=c("PatientID", "Population")) %>%
filter(!is.na(FACS)) %>%
filter(!PatientID %in% c("LN0262", "LN0302"))
cor_plots_facs <- list()
cor_plots_facs[["TFH"]] <- df_cor %>%
filter(Population=="TFH") %>%
ggplot(aes(x=FACS, y=RNA))+
geom_point(color=colors_umap_cl[["6"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["6"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson", size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,75), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,75), breaks = c(0, 25, 50, 75))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>-</sup><br><span>CXCR5<sup>+</sup> PD1<sup>+</sup></span>",
y="T<sub>FH</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TPR"]] <- df_cor %>%
filter(Population=="TPR") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["14"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["14"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,12), breaks = c(0, 4, 8, 12))+
scale_y_continuous(limits = c(0,12), breaks = c(0, 4, 8, 12))+
labs(
x="Ki67<sup>+</sup>",
y="T<sub>Pr</sub>",
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TDN"]] <- df_cor %>%
filter(Population=="TDN") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["19"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["19"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,8), breaks = c(0, 2, 4, 6, 8))+
scale_y_continuous(limits = c(0,8), breaks = c(0, 2, 4, 6, 8))+
labs(
x="CD45RA<sup>+</sup><br><span>CD4<sup>-</sup> CD8<sup>-</sub></span>",
y="T<sub>DN</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THCM1"]] <- df_cor %>%
filter(Population=="THCM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["2"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["2"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.35), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,17), breaks = c(0, 5, 10, 15))+
scale_y_continuous(limits = c(0,17), breaks = c(0, 5, 10, 15))+
labs(
x="CD45RA<sup>-</sup> FOXP3<sup>-</sup><br><span>CD69<sup>-</sup> PD1<sup>Low</sup></span>",
y="T<sub>H</sub> CM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THCM2"]] <- df_cor %>%
filter(Population=="THCM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["9"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["9"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,15), breaks = c(0, 5, 10, 15))+
scale_y_continuous(limits = c(0,15), breaks = c(0, 5, 10, 15))+
labs(
x="CD45RA<sup>-</sup> FOXP3<sup>-</sup><br><span>CD69<sup>+</sup> PD1<sup>Low</sup></span>",
y="T<sub>H</sub> CM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["THNaive"]] <- df_cor %>%
filter(Population=="THNaive") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["1"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["1"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,25), breaks = c(0, 8, 16, 24))+
scale_y_continuous(limits = c(0,25), breaks = c(0, 8, 16, 24))+
labs(
x="CD4<sup>+</sup> CD45RA<sup>+</sup>",
y="CD4<sup>+</sup> Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG"]] <- df_cor %>%
filter(Population=="TREG") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color="#578bb9", size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color="#578bb9", se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.45), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,45), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,45), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup>",
y="T<sub>REG</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/CM1"]] <- df_cor %>%
filter(Population=="TREG/CM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["8"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["8"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,55), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,55), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup><span> FOXP3<sup>+</sup> /<br>CD69<sup>-</sub></span>",
y="T<sub>REG</sub> CM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/EM2"]] <- df_cor %>%
filter(Population=="TREG/EM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["11"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["11"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,92), breaks = c(0, 30, 60, 90))+
scale_y_continuous(limits = c(0,92), breaks = c(0, 30, 60, 90))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>+</sub></span>",
y="T<sub>REG</sub> EM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/EM1"]] <- df_cor %>%
filter(Population=="TREG/EM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["15"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["15"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,50), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,50), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>-</sup> ICOS<sup>-</sup></span>",
y="T<sub>REG</sub> EM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TREG/CM2"]] <- df_cor %>%
filter(Population=="TREG/CM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["13"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["13"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,47), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,47), breaks = c(0, 15, 30, 45))+
labs(
x="CD4<sup>+</sup> FOXP3<sup>+</sup> /<br><span>CD69<sup>+</sup> IKZF3<sup>-</sup> ICOS<sup>+</sup></span>",
y="T<sub>REG</sub> CM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOXNaive"]] <- df_cor %>%
filter(Population=="TTOXNaive") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["12"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["12"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,27), breaks = c(0, 8, 16, 24))+
scale_y_continuous(limits = c(0,27), breaks = c(0, 8, 16, 24))+
labs(
x="CD8<sup>+</sup> CD45RA<sup>+</sup>",
y="CD8<sup>+</sup> Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX"]] <- df_cor %>%
filter(Population=="TTOX") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color="#b50923", size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color="#b50923", se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.45), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup>",
y="T<sub>TOX</sub> non-Naive"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM1"]] <- df_cor %>%
filter(Population=="TTOX/EM1") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["3"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["3"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.04), label.y.npc = c(0.9))+
scale_x_continuous(limits = c(0,85), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,85), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>-</sup> PD1<sup>Low</sup></span>",
y="T<sub>TOX</sub> EM<sub>1</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM2"]] <- df_cor %>%
filter(Population=="TTOX/EM2") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["16"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["16"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,57), breaks = c(0, 15, 30, 45))+
scale_y_continuous(limits = c(0,57), breaks = c(0, 15, 30, 45))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>-</sup> PD1<sup>High</sup></span>",
y="T<sub>TOX</sub> EM<sub>2</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
cor_plots_facs[["TTOX/EM3"]] <- df_cor %>%
filter(Population=="TTOX/EM3") %>%
ggplot(aes(x=FACS, y=RNA, label=substr(PatientID, 4, 6)))+
geom_point(color=colors_umap_cl[["5"]], size=0.45, alpha=0.75)+
geom_smooth(method = "lm", linetype="dashed", size=0.25, formula = y ~ x, na.rm = TRUE,
color=colors_umap_cl[["5"]], se=F, fullrange=T)+
stat_cor(aes(label=..r.label..), method = "pearson",size=2.5, label.x.npc = c(0.3), label.y.npc = c(0.1))+
scale_x_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
scale_y_continuous(limits = c(0,80), breaks = c(0, 25, 50, 75))+
labs(
x="CD31<sup>+</sup> U CD244<sup>+</sup> /<br><span>TIM3<sup>+</sup> PD1<sup>+</sup></span>",
y="T<sub>TOX</sub> EM<sub>3</sub>"
)+
theme_bw()+
coord_fixed()+
mytheme_1+
theme_axis_sub
```
## Median Pearson's R
```{r median coefficient}
df_cor %>%
group_by(Population) %>%
filter(!Population %in% c("TREG", "TTOX")) %>%
summarise(R=cor.test(RNA, FACS, method="pearson")$estimate) %>% pull(R) %>% median()
```
## Assemble plot
```{r assemble plot, fig.height=3}
plot_cor_full <- cor_plots_facs$TPR+labs(tag="B")+theme(plot.tag = element_text(margin = unit(c(0,0.15,0,0), units = "cm")))+
cor_plots_facs$THNaive+
cor_plots_facs$THCM1+
cor_plots_facs$THCM2+
cor_plots_facs$TFH+
cor_plots_facs$`TREG/CM1`+
cor_plots_facs$`TREG/CM2`+
cor_plots_facs$`TREG/EM1`+
cor_plots_facs$`TREG/EM2`+
cor_plots_facs$TTOXNaive+
cor_plots_facs$`TTOX/EM1`+
cor_plots_facs$`TTOX/EM2`+
cor_plots_facs$`TTOX/EM3`+
cor_plots_facs$TDN+
plot_layout(nrow = 2)
plot_cor_full
#ggsave(width = 18.5, height = 7, units = "cm", filename = "Figure2_p2.pdf")
```
# Mini scatter plot
```{r mini scatter, warning=FALSE, fig.height=2, fig.width=2}
set.seed(1)
FetchData(Combined_T, vars = c("integratedadt_.CD366", "integratedadt_.CD279", "IdentI")) %>%
sample_n(3000) %>%
mutate(IdentI=as.character(IdentI)) %>%
ggplot(aes(x=integratedadt_.CD366, y=integratedadt_.CD279, fill=IdentI, color=IdentI))+
ggrastr::geom_point_rast(size=0.25, stroke=0, shape=21, alpha=0.75, na.rm=T)+
scale_fill_manual(values = colors_umap_cl, limits=factor(cluster_order), labels=unlist(labels_cl))+
geom_rect(aes(xmin=0.95, xmax=2, ymin=0.25, ymax=3), size=0.15, fill=NA, color="black", linetype="solid")+
scale_y_continuous(limits = c(-0, 5), name = "Marker 1")+
scale_x_continuous(limits = c(-0, 2), name = "Marker 2")+
mytheme_1+
theme(axis.text = element_text(color="white"),
panel.border = element_rect(size=0.5),
axis.title.x = element_text(vjust = 7, size=8),
axis.title.y = element_text(vjust = -5, size=8),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank())
#ggsave(filename = "Figure2_p3.pdf", width = 3, height = 3, units = "cm")
```
# Session info
```{r session info}
sessionInfo()
```