Acute BDL (SFig. 2.6)
Last updated: 2021-02-27
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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 BDL 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-bdl"
output_path <- "output/mouse-acute-bdl"
# 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(str_to_upper(enzyme), levels = c("ALT", "AST", "ALP")))
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 |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
PCA
pca_result <- readRDS(here(output_path, "pca_result.rds"))
keys <- key_mm %>%
filter(treatment == "Bile Duct Ligation" & class == "Acute") %>%
distinct(time = value, label = time_label2) %>%
drop_na() %>%
mutate(time = ordered(time))
pca_plot <- pca_result$coords %>%
inner_join(keys, by = "time") %>%
mutate(treatment = case_when(
treatment == "bdl" ~ "BDL",
treatment == "sham" ~ "Sham Surgery"
)) %>%
ggplot(aes(x = PC1, y = PC2, color = label, shape = treatment, label = label)) +
geom_point() +
labs(
x = paste0("PC1", " (", pca_result$var[1], "%)"),
y = paste0("PC2", " (", pca_result$var[2], "%)"),
color = "Time", shape = "Group"
) +
my_theme(fsize = fz) +
theme(
legend.position = "top",
legend.box.margin = margin(10, 0, -20, 10)
) +
scale_color_manual(values = aachen_color(c(
"blue", "bordeaux", "orange",
"green"
))) +
scale_shape_manual(values = c(19, 15))
pca_plot
Volcano plot
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "bdl") %>%
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(1, 3, 7)
# 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 |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
volcano_supp
| Version | Author | Date |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "bdl") %>%
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(1, 3, 7)
# 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 |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
top_genes_supp
| Version | Author | Date |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
Time series cluster
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "bdl") %>%
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 Days") +
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 = 1)
)
ts_cluster
Collage
Supplementary Figure 2.6
sfig2_6 <- (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_6
ggsave(here("figures/Supplementary Figure 2.6.pdf"), sfig2_6,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.6.png"), sfig2_6,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 2.7
sfig2_7 <- (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_7
| Version | Author | Date |
|---|---|---|
| 6b6c16a | christianholland | 2020-12-23 |
ggsave(here("figures/Supplementary Figure 2.7.pdf"), sfig2_7,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.7.png"), sfig2_7,
width = 21, height = 10, units = c("cm")
)Time spend to execute this analysis: 01:01 minutes.
sessionInfo()
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