Acute APAP (Fig. 2)
Last updated: 2020-12-23
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Knit directory: meta-liver/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d4f78fa | christianholland | 2020-12-23 | wflow_publish("analysis/*.Rmd", delete_cache = T) |
| html | 6b6c16a | christianholland | 2020-12-23 | Build site. |
| Rmd | 9cdd995 | christianholland | 2020-12-22 | Added plotting scripts for mouse models |
| html | b5b58d0 | christianholland | 2020-12-22 | Build site. |
| Rmd | 7ca5d39 | christianholland | 2020-12-22 | added plotting script for apap |
Introduction
Here we generate publication ready plots of the analysis of the acute APAP mouse model.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(AachenColorPalette)
library(cowplot)
library(lemon)
library(ggpubr)
library(patchwork)
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/mouse-acute-apap"
output_path <- "output/mouse-acute-apap"
# graphical parameters
# fontsize
fz <- 7
# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))Design
design <- ggdraw() +
draw_image(here(data_path, "exp-design.pdf"))Histology
histology <- ggdraw() +
draw_image(here(data_path, "histology.png"))Liver enyzmes
df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
mutate(time = fct_inorder(time)) %>%
pivot_longer(col = -c(time, enzyme), names_to = "mouse", values_to = "y") %>%
mutate(enzyme = factor(str_to_upper(enzyme), levels = c("ALT", "AST")))
df_summary <- df %>%
group_by(time, enzyme) %>%
summarise(mean_se(y)) %>%
ungroup()
liver_enzymes <- df_summary %>%
ggplot(aes(x = time, y = y)) +
geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
geom_col() +
facet_rep_wrap(~enzyme, scales = "free", ncol = 1) +
labs(x = NULL, y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "Control", hide.ns = T
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
liver_enzymes
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
PCA
pca_result <- readRDS(here(output_path, "pca_result.rds"))
keys <- key_mm %>%
filter(treatment == "APAP" & class == "Acute") %>%
distinct(time = value, label = time_label2) %>%
drop_na() %>%
add_row(time = 0, label = "Control") %>%
mutate(time = ordered(time))
pca_plot <- pca_result$coords %>%
arrange(time) %>%
inner_join(keys, by = "time") %>%
mutate(label = fct_inorder(label)) %>%
ggplot(aes(x = PC1, y = PC2, color = label, label = label)) +
geom_point() +
labs(
x = paste0("PC1", " (", pca_result$var[1], "%)"),
y = paste0("PC2", " (", pca_result$var[2], "%)"),
color = "Time"
) +
my_theme(fsize = fz) +
theme(legend.position = "top",
legend.box.margin = margin(10, 0, -20, 0)) +
scale_color_manual(values = aachen_color(c(
"blue", "purple", "violet",
"bordeaux", "red", "orange",
"maygreen", "green", "turquoise",
"petrol"
)))
pca_plot
Volcano plot
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "apap") %>%
inner_join(key_mm, by = "contrast") %>%
select(-contrast) %>%
rename(contrast = time_label2) %>%
mutate(contrast = fct_drop(contrast)) %>%
mutate(regulation = fct_recode(regulation,
Up = "up", Down = "down",
n.s. = "ns"
))
deg_count <- df %>%
add_count(contrast, regulation) %>%
filter(regulation != "n.s.") %>%
mutate(regulation = fct_drop(regulation)) %>%
mutate(
logFC = case_when(
regulation == "Up" ~ 0.75 * max(logFC),
regulation == "Down" ~ 0.75 * min(logFC)
),
pval = 0.4
) %>%
distinct(n, contrast, logFC, pval, regulation, value) %>%
complete(contrast, nesting(regulation, logFC, pval), fill = list(n = 0))
main_time <- c(12, 24, 48)
# for main panel
volcano_main <- df %>%
filter(value %in% main_time) %>%
plot_volcano(nrow = 1) +
geom_text(
data = filter(deg_count, value %in% main_time),
aes(y = pval, label = n), size = fz / (14 / 5),
hjust = "inward", vjust = "inward",
show.legend = F
) +
theme(
legend.position = "top",
legend.box.margin = margin(-10, 0, -10, 0)
) +
labs(color = "Regulation") +
my_theme(grid = "y", fsize = fz)
# for supp panel
volcano_supp <- df %>%
filter(!value %in% main_time) %>%
plot_volcano(ncol = 1) +
geom_text(
data = filter(deg_count, !value %in% main_time),
aes(y = pval, label = n), size = fz / (14 / 5),
show.legend = F
) +
theme(
legend.position = "top",
legend.box.margin = margin(-10, 0, -10, 0)
) +
labs(color = "Regulation") +
my_theme(grid = "y", fsize = fz)
volcano_main
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
volcano_supp
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "apap") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2)
top_genes_df <- df %>%
filter(regulation != "ns") %>%
group_by(class, sign(logFC)) %>%
slice_max(order_by = abs(logFC), n = 10, with_ties = F) %>%
ungroup() %>%
nest(data = -c(class, value))
plots <- top_genes_df %>%
mutate(p = pmap(., .f = plot_top_genes, fontsize = fz))
main_time <- c(12, 24, 48)
# for main panel
top_genes_main <- plots %>%
filter(value %in% main_time) %>%
pull(p) %>%
wrap_plots() +
plot_layout(nrow = 1)
# for supp panel
top_genes_supp <- plots %>%
filter(!value %in% main_time) %>%
pull(p) %>%
wrap_plots() +
plot_layout(ncol = 1)
top_genes_main
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
top_genes_supp
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
Time series cluster
profile_label <- tribble(
~profile, ~process,
"STEM ID: 14", "Stress response",
"STEM ID: 9", "Migration",
"STEM ID: 12", "Proliferation",
"STEM ID: 6", "Metabolism (1)",
"STEM ID: 11", "Metabolism (2)",
"STEM ID: 18", "Metabolism (3)",
"STEM ID: 8", "Metabolism (4)"
) %>%
mutate(
profile = fct_inorder(profile),
process = coalesce(process, profile),
process = fct_inorder(process)
)
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "apap") %>%
filter(p <= 0.05) %>%
mutate(profile = fct_reorder(str_c(
"STEM ID: ",
as.character(profile)
), p)) %>%
inner_join(profile_label, by = "profile") %>%
select(-profile) %>%
rename(profile = process) %>%
filter(!time %in% c(384, 192, 144))
# extract meta data of profiles
profile_anno <- stem_res %>%
group_by(key, profile, p) %>%
mutate(y = 1.2 * abs(max(value))) %>%
ungroup() %>%
mutate(max_time = max(time)) %>%
distinct(key, profile, p, size, y, max_time) %>%
mutate(label = str_c(size, " ", "genes"))
ts_cluster <- stem_res %>%
plot_stem_profiles(model_profile = F, nrow = 1) +
labs(x = "Time in Hours") +
geom_text(
data = profile_anno, aes(x = 0, y = y, label = label),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward"
) +
my_theme(grid = "no", fsize = fz) +
scale_x_continuous(
breaks = unique(stem_res$time),
guide = guide_axis(n.dodge = 3)
)
ts_cluster
| Version | Author | Date |
|---|---|---|
| b5b58d0 | christianholland | 2020-12-22 |
Collage
Figure 2
fig2 <- (design + histology) /
((liver_enzymes | pca_plot) + plot_layout(widths = c(1,3))) /
((volcano_main / top_genes_main)) /
ts_cluster +
plot_layout(height = c(1.5, 2, 1.75, 0.75)) +
plot_annotation(tag_levels = list(c("A", "B", "C", "D", "E", "F", "", "", "",
"", "", "G"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig2
ggsave(here("figures/Figure 2.pdf"), fig2,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 2.png"), fig2,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 2.1
sfig2_1 <- (volcano_supp | top_genes_supp) +
plot_annotation(tag_levels = list(c("A", "B"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig2_1
ggsave(here("figures/Supplementary Figure 2.1.pdf"), sfig2_1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.1.png"), sfig2_1,
width = 21, height = 29.7, units = c("cm")
)Time spend to execute this analysis: 01:16 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
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