Skip to content

Commit

Permalink
Updated a number of figures.
Browse files Browse the repository at this point in the history
  • Loading branch information
@FloWuenne
FloWuenne committed Feb 29, 2024
1 parent b06dcd3 commit a49803c
Show file tree
Hide file tree
Showing 45 changed files with 6,528 additions and 3,279 deletions.
21 changes: 7 additions & 14 deletions analysis/figures.Figure1.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -235,6 +235,8 @@ library(dplyr)

# Get unique cell types
unique_cell_types <- sort(unique(meta_sample$anno_cell_type_lvl2))
max_x <- max(meta_sample$X_centroid)
max_y <- max(meta_sample$Y_centroid)

# Loop through each cell type and generate a plot
plots <- lapply(unique_cell_types, function(cell_type) {
Expand All @@ -244,8 +246,11 @@ plots <- lapply(unique_cell_types, function(cell_type) {
p <- ggplot(meta_sample, aes(x = X_centroid, y =
Y_centroid)) +
dark_theme_void() +
geom_point(data = subset(meta_sample,anno_cell_type_lvl2 != cell_type),
size = 0.6, color = "white") +
# Set x and y limits
xlim(0,max_x) +
ylim(0,max_y) +
# geom_point(data = subset(meta_sample,anno_cell_type_lvl2 != cell_type),
# size = 0.6, color = "white") +
geom_point(data = subset(meta_sample,anno_cell_type_lvl2 == cell_type),
size = 0.6,
aes(color = anno_cell_type_lvl2)) +
Expand Down Expand Up @@ -274,18 +279,6 @@ save_plot(cell_type_views,
```


```{r}
# Plot with labels
ggplot(meta_sample,aes(X_centroid,Y_centroid)) +

geom_point(aes(color = anno_cell_type_lvl2),size = 0.5) +
scale_color_manual(values = named_colors) +
facet_wrap(~ anno_cell_type_lvl2) +
theme(strip.text = element_text(size = 18, color = "white"),
legend.position = "none"
)
```

## Additional plots

# Figure 1D: Marker Dotplot (deprecated)
Expand Down
301 changes: 223 additions & 78 deletions analysis/figures.Figure2.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,10 @@ misty.results.g <- readRDS("./output/molkart/misty_results.lowres.125.rds")

# Figure 2A,D,G : Plot Misty interaction matrix

## With labels

```{r}
## Misty plots with labels
plot_interaction_heatmap_custom <- function(misty.results, view, cutoff = 1,
trim = -Inf, trim.measure = c(
"gain.R2", "multi.R2", "intra.R2",
Expand Down Expand Up @@ -148,113 +151,255 @@ plot_interaction_heatmap_custom <- function(misty.results, view, cutoff = 1,
control_misty <- plot_interaction_heatmap_custom(misty.results.g$control, "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

control_misty
#unique(control_misty$Predictor)

save_plot(control_misty,
file = "./plots/Figure2.mistyR_control.pdf",
base_height = 5)
file = "./plots/Figure2.mistyR_control.with_labels.pdf",
base_height = 7)


d2_misty <- plot_interaction_heatmap_custom(misty.results.g$'2d', "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

d2_misty

save_plot(d2_misty,
file = "./plots/Figure2.mistyR_d2.pdf",
file = "./plots/Figure2.mistyR_d2.with_labels.pdf",
base_height = 5)

d4_misty <- plot_interaction_heatmap_custom(misty.results.g$'4d', "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

d4_misty

save_plot(d4_misty,
file = "./plots/Figure2.mistyR_d4.pdf",
file = "./plots/Figure2.mistyR_d4.with_labels.pdf",
base_height = 5)
```

# Figure 2 : Distribution of cell types of interest

## Without labels

```{r}
## Misty figures without text for adding to Adobe
## Misty plots with labels
plot_interaction_heatmap_custom <- function(misty.results, view, cutoff = 1,
trim = -Inf, trim.measure = c(
"gain.R2", "multi.R2", "intra.R2",
"gain.RMSE", "multi.RMSE", "intra.RMSE"
),
clean = FALSE) {
trim.measure.type <- match.arg(trim.measure)

assertthat::assert_that(("importances.aggregated" %in% names(misty.results)),
msg = "The provided result list is malformed. Consider using collect_results()."
)

assertthat::assert_that(("improvements.stats" %in% names(misty.results)),
msg = "The provided result list is malformed. Consider using collect_results()."
)

# Potential supplementary figure with markers
assertthat::assert_that((view %in%
(misty.results$importances.aggregated %>% dplyr::pull(view))),
msg = "The selected view cannot be found in the results table."
)

```{r}
library(data.table)

spots_control <- fread("../results/nf-core_molkart/mindagap/sample_control_r2_s1_sample_control_r2_s1.spots_markedDups.txt")
colnames(spots_control) <- c("X","Y","Z","gene")
nppa <- ggplot(spots_control,aes(X,Y)) +
geom_point(data = subset(spots_control,gene =="Pln"),color = "lightgrey",size = 0.00001) +
geom_point(data = subset(spots_control,gene =="Nppa"),color = "magenta",size = 0.00001) +
xlim(0,17152) +
ylim(0,8576) +
dark_theme_void()

myeloid <- ggplot(spots_control,aes(X,Y)) +
geom_point(data = subset(spots_control,gene =="C1qa"),color = "yellow",size = 0.00001) +
xlim(0,17152) +
ylim(0,8576) +
dark_theme_void()

endocard <- ggplot(spots_control,aes(X,Y)) +
geom_point(data = subset(spots_control,gene =="Npr3"),color = "cyan",size = 0.00001) +
xlim(0,17152) +
ylim(0,8576) +
dark_theme_void()

nppa | myeloid | endocard
```
inv <- sign((stringr::str_detect(trim.measure.type, "gain") |
stringr::str_detect(trim.measure.type, "RMSE", negate = TRUE)) - 0.5)

```{r}
spots_d2 <- fread("../results/nf-core_molkart/mindagap/sample_2d_r2_s1_sample_2d_r2_s1.spots_markedDups.txt")
colnames(spots_d2) <- c("X","Y","Z","gene")
nppa <- ggplot(spots_d2,aes(X,Y)) +
geom_point(data = subset(spots_d2,gene =="Nppa"),color = "magenta",size = 0.1) +
xlim(0,10720) +
ylim(0,25728) +
dark_theme_void()

myeloid <- ggplot(spots_d2,aes(X,Y)) +
geom_point(data = subset(spots_d2,gene =="C1qa"),color = "yellow",size = 0.1) +
xlim(0,10720) +
ylim(0,25728) +
dark_theme_void()

endocard <- ggplot(spots_d2,aes(X,Y)) +
geom_point(data = subset(spots_d2,gene =="Npr3"),color = "cyan",size = 0.1) +
xlim(0,10720) +
ylim(0,25728) +
dark_theme_void()

nppa | myeloid | endocard
```
targets <- misty.results$improvements.stats %>%
dplyr::filter(
measure == trim.measure.type,
inv * mean >= inv * trim
) %>%
dplyr::pull(target)

```{r}
spots_d4 <- fread("../results/nf-core_molkart/mindagap/sample_4d_r1_s1_sample_4d_r1_s1.spots_markedDups.txt")
colnames(spots_d4) <- c("X","Y","Z","gene")
nppa <- ggplot(spots_d4,aes(X,Y)) +
geom_point(data = subset(spots_d4,gene =="Nppa"),color = "magenta",size = 0.1) +
xlim(0,15008) +
ylim(0,19296) +
dark_theme_void()

myeloid <- ggplot(spots_d4,aes(X,Y)) +
geom_point(data = subset(spots_d4,gene =="C1qa"),color = "yellow",size = 0.1) +
xlim(0,15008) +
ylim(0,19296) +
dark_theme_void()

endocard <- ggplot(spots_d4,aes(X,Y)) +
geom_point(data = subset(spots_d4,gene =="Npr3"),color = "cyan",size = 0.1) +
xlim(0,15008) +
ylim(0,19296) +
dark_theme_void()

nppa | myeloid | endocard

plot.data <- misty.results$importances.aggregated %>%
dplyr::filter(view == !!view, Target %in% targets)

if (clean) {
clean.predictors <- plot.data %>%
dplyr::mutate(Importance = Importance * (Importance >= cutoff)) %>%
dplyr::group_by(Predictor) %>%
dplyr::summarize(total = sum(Importance, na.rm = TRUE)) %>%
dplyr::filter(total > 0) %>%
dplyr::pull(Predictor)
clean.targets <- plot.data %>%
dplyr::mutate(Importance = Importance * (Importance >= cutoff)) %>%
dplyr::group_by(Target) %>%
dplyr::summarize(total = sum(Importance, na.rm = TRUE)) %>%
dplyr::filter(total > 0) %>%
dplyr::pull(Target)
plot.data.clean <- plot.data
# plot.data.clean <- plot.data %>%
# dplyr::filter(
# Predictor %in% clean.predictors,
# Target %in% clean.targets
# )
} else {
plot.data.clean <- plot.data
}

#set2.blue <- "#8DA0CB"
## Color roughly based on https://icolorpalette.com/color/080210


## Replace dots with spaces in cell type names
plot.data.clean$Predictor <- gsub("\\."," ",plot.data.clean$Predictor)
plot.data.clean$Target <- gsub("\\."," ",plot.data.clean$Target)

plot.data.clean$Predictor <- gsub("Cardiomyocytes Nppa ","Cardiomyocytes Nppa+",plot.data.clean$Predictor)
plot.data.clean$Target <- gsub("Cardiomyocytes Nppa ","Cardiomyocytes Nppa+",plot.data.clean$Target)

## Subset for only relevant Predictors
plot.data.clean <- subset(plot.data.clean,Predictor %in% c("Cardiomyocytes","Cardiomyocytes Nppa+","Endocardial cells",
"Cardiac fibroblasts","Pericytes","Myeloid cells"))

## Subset for only interactions above specified threshold
plot.data.clean <- plot.data.clean %>%
mutate("Importance" = ifelse(Importance < cutoff, 0, Importance))

## Plot data
results.plot <- ggplot2::ggplot(
plot.data.clean,
ggplot2::aes(
x = Predictor,
y = Target
)
) +
#ggplot2::geom_tile(data = subset(plot.data.clean, Importance > cutoff),ggplot2::aes(fill = Importance)) +
ggplot2::geom_tile(ggplot2::aes(fill = Importance)) +
ggplot2::scale_fill_gradientn(
colours = c("white", "#efe5fb", "#d3baf6", "#691ad2","#5314a6","#27094f"),
#values = scales::rescale(c(0, 0.5, 1, 1.2)),
limits = c(0,1.8)
) +
# ggplot2::scale_fill_gradient2(

# limits = c(0, max(plot.data.clean$Importance))
# ) +
ggplot2::theme_classic() +
ggplot2::theme(axis.title = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
legend.title = element_blank(),
legend.text = element_blank()) +
ggplot2::coord_equal()
#ggplot2::ggtitle(view)

return(results.plot)
#return(plot.data.clean)

invisible(misty.results)
}

## Now we will plot the interaction heatmap
control_misty <- plot_interaction_heatmap_custom(misty.results.g$control, "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

control_misty

save_plot(control_misty,
file = "./plots/Figure2.mistyR_control.pdf",
base_height = 2.5)


d2_misty <- plot_interaction_heatmap_custom(misty.results.g$'2d', "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

d2_misty

save_plot(d2_misty,
file = "./plots/Figure2.mistyR_d2.pdf",
base_height = 2.5)

d4_misty <- plot_interaction_heatmap_custom(misty.results.g$'4d', "paraview", cutoff = 0.4, clean = TRUE, trim = 5)

d4_misty

save_plot(d4_misty,
file = "./plots/Figure2.mistyR_d4.pdf",
base_height = 2.5)
```


# Figure 2 : Distribution of cell types of interest


<!-- # Potential supplementary figure with markers -->

<!-- ```{r} -->
<!-- library(data.table) -->

<!-- spots_control <- fread("../results/nf-core_molkart/mindagap/sample_control_r2_s1_sample_control_r2_s1.spots_markedDups.txt") -->
<!-- colnames(spots_control) <- c("X","Y","Z","gene") -->
<!-- nppa <- ggplot(spots_control,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_control,gene =="Pln"),color = "lightgrey",size = 0.00001) + -->
<!-- geom_point(data = subset(spots_control,gene =="Nppa"),color = "magenta",size = 0.00001) + -->
<!-- xlim(0,17152) + -->
<!-- ylim(0,8576) + -->
<!-- dark_theme_void() -->

<!-- myeloid <- ggplot(spots_control,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_control,gene =="C1qa"),color = "yellow",size = 0.00001) + -->
<!-- xlim(0,17152) + -->
<!-- ylim(0,8576) + -->
<!-- dark_theme_void() -->

<!-- endocard <- ggplot(spots_control,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_control,gene =="Npr3"),color = "cyan",size = 0.00001) + -->
<!-- xlim(0,17152) + -->
<!-- ylim(0,8576) + -->
<!-- dark_theme_void() -->

<!-- nppa | myeloid | endocard -->
<!-- ``` -->

<!-- ```{r} -->
<!-- spots_d2 <- fread("../results/nf-core_molkart/mindagap/sample_2d_r2_s1_sample_2d_r2_s1.spots_markedDups.txt") -->
<!-- colnames(spots_d2) <- c("X","Y","Z","gene") -->
<!-- nppa <- ggplot(spots_d2,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d2,gene =="Nppa"),color = "magenta",size = 0.1) + -->
<!-- xlim(0,10720) + -->
<!-- ylim(0,25728) + -->
<!-- dark_theme_void() -->

<!-- myeloid <- ggplot(spots_d2,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d2,gene =="C1qa"),color = "yellow",size = 0.1) + -->
<!-- xlim(0,10720) + -->
<!-- ylim(0,25728) + -->
<!-- dark_theme_void() -->

<!-- endocard <- ggplot(spots_d2,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d2,gene =="Npr3"),color = "cyan",size = 0.1) + -->
<!-- xlim(0,10720) + -->
<!-- ylim(0,25728) + -->
<!-- dark_theme_void() -->

<!-- nppa | myeloid | endocard -->
<!-- ``` -->

<!-- ```{r} -->
<!-- spots_d4 <- fread("../results/nf-core_molkart/mindagap/sample_4d_r1_s1_sample_4d_r1_s1.spots_markedDups.txt") -->
<!-- colnames(spots_d4) <- c("X","Y","Z","gene") -->
<!-- nppa <- ggplot(spots_d4,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d4,gene =="Nppa"),color = "magenta",size = 0.1) + -->
<!-- xlim(0,15008) + -->
<!-- ylim(0,19296) + -->
<!-- dark_theme_void() -->

<!-- myeloid <- ggplot(spots_d4,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d4,gene =="C1qa"),color = "yellow",size = 0.1) + -->
<!-- xlim(0,15008) + -->
<!-- ylim(0,19296) + -->
<!-- dark_theme_void() -->

<!-- endocard <- ggplot(spots_d4,aes(X,Y)) + -->
<!-- geom_point(data = subset(spots_d4,gene =="Npr3"),color = "cyan",size = 0.1) + -->
<!-- xlim(0,15008) + -->
<!-- ylim(0,19296) + -->
<!-- dark_theme_void() -->

<!-- nppa | myeloid | endocard -->
<!-- ``` -->


<!-- # Figure 2A: Plot whole tissue quantification changes across time -->

<!-- ```{r} -->
Expand Down
Loading

0 comments on commit a49803c

Please sign in to comment.