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Addressed comments R1-1 and R2-4 #10

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Addressed comments R1-1 and R2-4 #10

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6478404
Updated Supp Figure 5 and added celltype analysis markdown R1-1 and F…
FloWuenne Dec 4, 2024
5e3046c
Updated Figure 3 and Supp Figure 7 with new region quantifications.
FloWuenne Dec 15, 2024
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107 changes: 77 additions & 30 deletions analysis/figures.Figure3.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -17,49 +17,96 @@ library(ggbeeswarm)
source("./code/functions.R")
```

# Load data

```{r}
abs_counts <- fread("./data/Traditional_IF_absolute_cell_counts.csv")
colnames(abs_counts) <- gsub(" ","_",colnames(abs_counts))
abs_counts <- abs_counts %>%
pivot_longer(cols = Endo_Pre:BZ_2d,
names_to = "sample",
values_to = "cell_count") %>%
separate(sample, into = c("region","timepoint"), sep = "_") %>%
subset(timepoint != "Pre")
## Read in quantified data
all_cell_types_quantified <- fread("./review/all_cell_types_per_region_counts_perctentages_20241212.csv")

ccr2_monomacro <- all_cell_types_quantified %>%
subset(final_cell_type == "Mono / Macros Ccr2+") %>%
subset(timepoint %in% c("4h","24h","48h")) %>%
subset(region_name %in% c("border_zone","infarct_core","epicardial_region","endocardial_region"))

# Replace abbreviations with full labels
abs_counts$region <- gsub("Endo","Endocard",abs_counts$region)
abs_counts$region <- gsub("Core","Infarct core",abs_counts$region)
abs_counts$region <- gsub("Epi","Epicard",abs_counts$region)
abs_counts$region <- gsub("BZ","Border zone",abs_counts$region)
abs_counts$region <- factor(abs_counts$region, levels = c("Endocard","Infarct core","Epicard","Border zone"))
ccr2_monomacro$region_name <- gsub("border_zone","Border zone",ccr2_monomacro$region_name)
ccr2_monomacro$region_name <- gsub("infarct_core","Infarct core",ccr2_monomacro$region_name)
ccr2_monomacro$region_name <- gsub("epicardial_region","Epicardium",ccr2_monomacro$region_name)
ccr2_monomacro$region_name <- gsub("endocardial_region","Endocardium",ccr2_monomacro$region_name)

abs_counts$timepoint <- gsub("1d","24h",abs_counts$timepoint)
abs_counts$timepoint <- gsub("2d","2 days",abs_counts$timepoint)
abs_counts$timepoint <- factor(abs_counts$timepoint, levels = c("Pre","4h","24h","2 days"))
ccr2_monomacro$region_name <- factor(ccr2_monomacro$region_name,
levels = c("Endocardium","Infarct core","Epicardium","Border zone"))
ccr2_monomacro$timepoint <- factor(ccr2_monomacro$timepoint,
levels = c("4h","24h","48h"))

abs_plot <- ggplot(abs_counts,aes(timepoint,cell_count)) +
seqIF_ccr2_relquant <- ggplot(ccr2_monomacro,aes(x = timepoint,y = count_per_mm2)) +
stat_summary(
fun.y = mean,
geom = "bar",
width = 0.9,
size = 0.3,
color = "black",
fill = "lightgrey") +
labs(y = "Absolute cell number") +
geom_beeswarm(size = 2 , pch = 21, color = "black", aes(fill = region)) +
facet_grid(.~ region) +
geom_beeswarm(size = 2, pch = 21, color = "black", aes(fill = region_name)) +
labs(x = "Time",
y = expression("Cells /"~mm^2)) +
#expression(paste("Mo / M",phi," per "~mm^2))
facet_grid(. ~ region_name) +
scale_fill_manual(values = c("#337272","#f0f0f0","#b062c2","#2c95c5")) +
theme(axis.title = element_text(face="bold"),
legend.position = "none") +
scale_fill_manual(values = c("#337272","#f0f0f0","#b062c2","#2c95c5")) +
theme(panel.border = element_rect(color = "black", fill = NA, size = 0.75)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank())

save_plot(filename = "./plots/Figure_3.tradIF-absolute_cell_counts.pdf",
plot = abs_plot,
base_asp = 1.8,
base_height = 3)
axis.title.x = element_blank())
seqIF_ccr2_relquant

save_plot(filename = "./plots/Figure_3.SeqIF-absolute_cell_counts.pdf",
plot = seqIF_ccr2_relquant,
base_asp = 1.8,
base_height = 3)
```




<!-- # ```{r} -->
<!-- # abs_counts <- fread("./data/Traditional_IF_absolute_cell_counts.csv") -->
<!-- # colnames(abs_counts) <- gsub(" ","_",colnames(abs_counts)) -->
<!-- # abs_counts <- abs_counts %>% -->
<!-- # pivot_longer(cols = Endo_Pre:BZ_2d, -->
<!-- # names_to = "sample", -->
<!-- # values_to = "cell_count") %>% -->
<!-- # separate(sample, into = c("region","timepoint"), sep = "_") %>% -->
<!-- # subset(timepoint != "Pre") -->
<!-- # -->
<!-- # # Replace abbreviations with full labels -->
<!-- # abs_counts$region <- gsub("Endo","Endocard",abs_counts$region) -->
<!-- # abs_counts$region <- gsub("Core","Infarct core",abs_counts$region) -->
<!-- # abs_counts$region <- gsub("Epi","Epicard",abs_counts$region) -->
<!-- # abs_counts$region <- gsub("BZ","Border zone",abs_counts$region) -->
<!-- # abs_counts$region <- factor(abs_counts$region, levels = c("Endocard","Infarct core","Epicard","Border zone")) -->
<!-- # -->
<!-- # abs_counts$timepoint <- gsub("1d","24h",abs_counts$timepoint) -->
<!-- # abs_counts$timepoint <- gsub("2d","2 days",abs_counts$timepoint) -->
<!-- # abs_counts$timepoint <- factor(abs_counts$timepoint, levels = c("Pre","4h","24h","2 days")) -->
<!-- # -->
<!-- # abs_plot <- ggplot(abs_counts,aes(timepoint,cell_count)) + -->
<!-- # stat_summary( -->
<!-- # fun.y = mean, -->
<!-- # geom = "bar", -->
<!-- # width = 0.9, -->
<!-- # size = 0.3, -->
<!-- # color = "black", -->
<!-- # fill = "lightgrey") + -->
<!-- # labs(y = "Absolute cell number") + -->
<!-- # geom_beeswarm(size = 2 , pch = 21, color = "black", aes(fill = region)) + -->
<!-- # facet_grid(.~ region) + -->
<!-- # theme(axis.title = element_text(face="bold"), -->
<!-- # legend.position = "none") + -->
<!-- # scale_fill_manual(values = c("#337272","#f0f0f0","#b062c2","#2c95c5")) + -->
<!-- # theme(panel.border = element_rect(color = "black", fill = NA, size = 0.75)) + -->
<!-- # theme(axis.text.x = element_text(angle = 45, hjust = 1), -->
<!-- # axis.title.x = element_blank()) -->
<!-- # -->
<!-- # save_plot(filename = "./plots/Figure_3.tradIF-absolute_cell_counts.pdf", -->
<!-- # plot = abs_plot, -->
<!-- # base_asp = 1.8, -->
<!-- # base_height = 3) -->
```
151 changes: 142 additions & 9 deletions analysis/figures.supplementary_figure_5.molkart_spatial_plots.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -92,8 +92,8 @@ sample_plots <- lapply(sample_names, function(sample) {
scale_color_manual(values = named_colors
) +
theme(legend.position = "right",
legend.title = element_text(size = 20),
legend.text = element_text(size = 16)) + # Hide legend or adjust as needed
legend.title = element_text(size = 15),
legend.text = element_text(size = 14)) + # Hide legend or adjust as needed
guides(color = guide_legend(title = "Cell types",override.aes = list(size = 6))) +
labs(title = "")

Expand All @@ -104,28 +104,28 @@ sample_plots <- lapply(sample_names, function(sample) {
title = "Control",
y = "Replicate 1") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 20, face = "bold"),
axis.title.y = element_text(size = 20,angle = 90, face = "bold")
plot.title = element_text(hjust=0.5, size = 15, face = "bold"),
axis.title.y = element_text(size = 15,angle = 90, face = "bold")
)
} else if(sample == "sample_4h_r1_s1"){
p <- p +
labs(title = "4 hours") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 20, face = "bold"))
plot.title = element_text(hjust=0.5, size = 15, face = "bold"))
} else if(sample == "sample_2d_r1_s1"){
p <- p +
labs(title = "2 days") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 20, face = "bold"))
plot.title = element_text(hjust=0.5, size = 15, face = "bold"))
} else if(sample == "sample_4d_r1_s1"){
p <- p +
labs(title = "4 days") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 20, face = "bold"))
plot.title = element_text(hjust=0.5, size = 15, face = "bold"))
} else if (sample == "sample_control_r2_s1"){
p <- p +
labs(y = "Replicate 2") +
theme(axis.title.y = element_text(size = 20,angle = 90, face = "bold")
theme(axis.title.y = element_text(size = 15,angle = 90, face = "bold")
)
}

Expand Down Expand Up @@ -195,10 +195,143 @@ ct_time_barplot
```



## C) Dotplot for subclustering of immune cell types

```{r}
immune_cells <- subset(seurat_object,
anno_cell_type_lvl2 %in% c("Myeloid_cells","Lymphoid_cells"))
```

```{r}
library(viridis)
immune_cells_dotplot <- DotPlot(immune_cells,
features = c("Csf3r","Ccl2","C1qa","Arg1","Cd3e","Ighm","Cd74"),
group.by = "anno_cell_type_lvl3") +
RotatedAxis() +
scale_colour_viridis(option="magma") +
geom_point(aes(size=pct.exp), shape = 21, colour="black", stroke=0.5) +
guides(size=guide_legend(override.aes=list(shape=21, colour="black", fill="black"))) +
labs(tags = "c") + theme(plot.tag = element_text(size = 20, face = "bold"))
immune_cells_dotplot
```


## D) Spatial plot for immune subclustering

```{r}
## Perform similar rotation correction on images as in main Supplementary Figure 5
metadata <- [email protected]

## Set sample order for patchwork
sample_names <- c("sample_4h_r1_s1",
"sample_2d_r1_s1",
"sample_4h_r2_s2",
"sample_2d_r2_s1")

new_metadata <- data.frame()
# For each sample in sample_names, apply a rotation function to x and y positions in metadata
for(sample in sample_names){
meta_sub <- subset(metadata,sample_ID == sample)
if(sample == "sample_control_r1_s1"){ # rotate 90 degrees counter clockwise
y_orig <- meta_sub$Y_centroid
x_orig <- meta_sub$X_centroid
meta_sub$X_centroid <- y_orig * -1
meta_sub$Y_centroid <- x_orig
}else if(sample == "sample_control_r2_s1"){ # rotate 90 degrees counter clockwise
meta_sub$X_centroid <- meta_sub$X_centroid * -1
meta_sub$Y_centroid <- meta_sub$Y_centroid * 1
}else if(sample == "sample_4h_r1_s1"){ # rotate 90 degrees counter clockwise
meta_sub$X_centroid <- meta_sub$X_centroid * -1
meta_sub$Y_centroid <- meta_sub$Y_centroid * 1
}else if(sample == "sample_4h_r2_s2"){ # rotate 90 degrees counter clockwise
y_orig <- meta_sub$Y_centroid
x_orig <- meta_sub$X_centroid
meta_sub$X_centroid <- y_orig * -1
meta_sub$Y_centroid <- x_orig
}else if(sample == "sample_2d_r1_s1"){ # rotate 90 degrees counter clockwise
meta_sub$X_centroid <- meta_sub$X_centroid * -1
meta_sub$Y_centroid <- meta_sub$Y_centroid * -1
}else if(sample == "sample_4d_r2_s1"){ # rotate 90 degrees counter clockwise
y_orig <- meta_sub$Y_centroid
meta_sub$Y_centroid <- meta_sub$X_centroid * -1
meta_sub$X_centroid <- y_orig * 1
}
new_metadata <- rbind(new_metadata,meta_sub)
}
new_metadata
```


```{r}
## Make a spatial plot for each sample and organize them using patchwork
sample_plots <- lapply(sample_names, function(sample) {
meta_sub <- subset(new_metadata,sample_ID == sample)
meta_sub$anno_cell_type_lvl3 <- gsub("_"," ",meta_sub$anno_cell_type_lvl3)

# Generate the plot
## Add a line of blank space on top of the plot
p <- ggplot(meta_sub, aes(x = X_centroid,
y = Y_centroid,
color = anno_cell_type_lvl3)) +
dark_theme_void() +
geom_point(size = 0.4) +
theme(legend.position = "right",
legend.title = element_text(size = 15),
legend.text = element_text(size = 14)) + # Hide legend or adjust as needed
guides(color = guide_legend(title = "Immune cell types",
override.aes = list(size = 6))) +
labs(title = "")

## Manually add tags and titles to plots for visual improvements
if(sample == "sample_control_r1_s1"){
p <- p +
labs(tag = "a",
title = "Control",
y = "Replicate 1") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 15, face = "bold"),
axis.title.y = element_text(size = 15,angle = 90, face = "bold")
)
} else if(sample == "sample_4h_r1_s1"){
p <- p +
labs(title = "4 hours") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 15, face = "bold")) +
labs(tags = "d") + theme(plot.tag = element_text(size = 20, face = "bold"))
} else if(sample == "sample_2d_r1_s1"){
p <- p +
labs(title = "2 days") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 15, face = "bold"))
} else if(sample == "sample_4d_r1_s1"){
p <- p +
labs(title = "4 days") +
theme(plot.tag = element_text(size = 20, face = "bold"),
plot.title = element_text(hjust=0.5, size = 15, face = "bold"))
} else if (sample == "sample_control_r2_s1"){
p <- p +
labs(y = "Replicate 2") +
theme(axis.title.y = element_text(size = 15,angle = 90, face = "bold")
)
}


# Return the plot
return(p)
})

spatial_plot_immune_cells <- wrap_plots(sample_plots,ncol = 2, nrows = 2) + plot_layout(guides = "collect") & theme(legend.position = 'right')
spatial_plot_immune_cells
```



## Merge plots

```{r}
wrapped_plots <- (sample_plots_arranged / ct_time_barplot) + plot_layout(nrow = 2, ncol = 1)
wrapped_plots <- sample_plots_arranged / ct_time_barplot / (immune_cells_dotplot | spatial_plot_immune_cells) +
plot_layout(nrow = 3, ncol = 1)
wrapped_plots

save_plot(wrapped_plots,
Expand Down
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