Acute CCl4 (SFig. 2.2)
Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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Introduction
Here we generate publication ready plots of the analysis of the acute CCl4 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-ccl4"
output_path <- "output/mouse-acute-ccl4"
# 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), names_to = "enzyme", values_to = "y") %>%
mutate(enzyme = factor(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
PCA
pca_result <- readRDS(here(output_path, "pca_result.rds"))
keys <- key_mm %>%
filter(treatment == "CCl4" & 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 == "ccl4") %>%
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(8,24,48)
# 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
volcano_supp
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "ccl4") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2)
top_genes_df <- df %>%
group_by(class, sign(logFC)) %>%
slice_max(order_by = abs(statistic), 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(8,24,48)
# 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
top_genes_supp
Time series cluster
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "ccl4") %>%
filter(p <= 0.05) %>%
mutate(profile = fct_reorder(str_c(
"STEM ID: ",
as.character(profile)
), p))
# 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
Collage
Supplementary Figure 2.2
sfig2_2 <- (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")
)
sfig2_2
ggsave(here("figures/Supplementary Figure 2.2.pdf"), sfig2_2,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.2.png"), sfig2_2,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 2.3
sfig2_3 <- (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_3
ggsave(here("figures/Supplementary Figure 2.3.pdf"), sfig2_3,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.3.png"), sfig2_3,
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)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices datasets utils methods base
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#> other attached packages:
#> [1] patchwork_1.1.1 ggpubr_0.4.0 lemon_0.4.5
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