Chronic CCl4 (Fig. 1)
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 chronic 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(VennDiagram)
library(grid)
library(gridExtra)
library(patchwork)
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/mouse-chronic-ccl4"
output_path <- "output/mouse-chronic-ccl4"
# graphical parameters
# fontsize
fz <- 9
# 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")) +
theme(plot.margin = margin(r = 1, unit = "cm"))Histology
histology <- ggdraw() +
draw_image(here(data_path, "histology.png"))Liver enyzmes
df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
mutate(time = parse_number(time),
time = replace_na(time, 0)) %>%
mutate(time = ordered(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_partial <- df %>%
ggplot(aes(x = time, y = y)) +
geom_boxplot() +
geom_jitter(alpha = 0.5) +
# geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
# geom_col() +
facet_rep_wrap(~enzyme, scales = "free", ncol = 3) +
labs(x = "Time in month", y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "0", hide.ns = T
)
liver_enzymes <- df %>%
ggplot(aes(x = time, y = y)) +
geom_boxplot() +
geom_jitter(alpha = 0.5) +
# geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
# geom_col() +
facet_rep_wrap(~enzyme, scales = "free", ncol = 1) +
labs(x = "Time in month", y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "0", hide.ns = T
)
liver_enzymes
PCA
pca_result <- readRDS(here(output_path, "pca_result.rds"))
keys <- key_mm %>%
filter(treatment == "CCl4" & class == "Chronic") %>%
distinct(time = value, label = time_label2) %>%
drop_na() %>%
add_row(time = 0, label = "Control") %>%
mutate(time = ordered(time))
pca_plot <- pca_result$coords %>%
arrange(time) %>%
inner_join(keys, by = "time") %>%
mutate(label = fct_inorder(label)) %>%
mutate(treatment = case_when(treatment == "oil" ~ "Oil",
treatment == "ccl4" ~ "CCl4 + Oil",
treatment == "ctrl" ~ "Control")) %>%
ggplot(aes(x=PC1, y=PC2, color=label, shape = treatment, label = label)) +
geom_point() +
labs(x = paste0("PC1", " (", pca_result$var[1], "%)"),
y = paste0("PC2", " (", pca_result$var[2], "%)"),
color = "Time", shape = "Treatment") +
my_theme(fsize = fz) +
theme(legend.position = "top",
legend.box.margin = margin(10, 0, -20, 10)) +
scale_color_manual(values = aachen_color(c(
"violet", "bordeaux", "red", "orange"
)))
pca_plot
Volcano plot
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_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))
volcano <- df %>%
plot_volcano(ncol = 1) +
geom_text(
data = filter(deg_count),
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)
volcano
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_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))
# for main panel
top_genes <- plots %>%
pull(p) %>%
wrap_plots() +
plot_layout(ncol = 1)
top_genes
Gene overlap
df <- readRDS(here(output_path, "limma_result.rds"))
tables = df %>%
filter(contrast_reference == "pure_ccl4") %>%
mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
select(-contrast_reference, -contrast) %>%
mutate(class = factor(class,
levels = c("Month 2", "Month 6", "Month 12"))) %>%
group_split(class)
# extract labellers
c1 = tables[[1]] %>% distinct(class) %>% pull() %>% as.character()
c2 = tables[[2]] %>% distinct(class) %>% pull() %>% as.character()
c3 = tables[[3]] %>% distinct(class) %>% pull() %>% as.character()
t1 = tables[[1]] %>% count(regulation)
t2 = tables[[2]] %>% count(regulation)
t3 = tables[[3]] %>% count(regulation)
plots = c("up", "down") %>%
map(function(r) {
# set sizes of regulated genes
a1 = t1 %>% filter(regulation == r) %>% pull(n)
a2 = t2 %>% filter(regulation == r) %>% pull(n)
a3 = t3 %>% filter(regulation == r) %>% pull(n)
a12 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a23 = purrr::reduce(
list(tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a13 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a123 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
grid.newpage()
p = draw.triple.venn(
area1 = a1, area2 = a2, area3 = a3,
n12 = a12, n23 = a23, n13 = a13,
n123 = a123,
category = c(c1, c2, c3),
# lty = "blank",
cex = 1/12*fz,
fontfamily = rep("sans", 7),
# fill = aachen_color(c("purple", "petrol", "red")),
# cat.col = aachen_color(c("purple", "petrol", "red")),
cat.cex = 1/12*(fz+1),
cat.fontfamily = rep("sans", 3),
cat.pos = c(350,10,180),
cat.prompts = T,
cat.just = list(c(0.5, 1), c(0.5, 1), c(0.5, 1))
) %>%
as_ggplot() %>%
grid.arrange(top = textGrob(str_to_title(r), gp=gpar(fontsize=fz,
fontface = "bold")))
})
gene_overlap = wrap_plots(plots)Top gene of overlap
df = readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_ccl4") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2) %>%
filter(regulation != "ns") %>%
mutate(regulation = fct_inorder(str_to_title(regulation)))
top_genes_ranked = df %>%
# filter for genes that are deregulated at all time points
group_by(gene, regulation) %>%
filter(n() == 3) %>%
summarise(mean_logfc = mean(logFC)) %>%
group_by(regulation) %>%
mutate(rank = row_number(-abs(mean_logfc))) %>%
ungroup()
top_genes_of_overlap = df %>%
inner_join(top_genes_ranked, by=c("gene", "regulation"))
top_overlap_genes = top_genes_of_overlap %>%
filter(rank <= 5) %>%
ggplot(aes(x=fct_reorder(gene, mean_logfc), y=logFC, group = class,
fill = class)) +
geom_col(position = "dodge") +
facet_rep_wrap(~regulation, ncol = 1, scales = "free") +
my_theme(grid = "y", fsize = fz) +
labs(x="Gene", y="logFC", fill = NULL) +
theme(legend.position = "top",
legend.box.margin = margin(-2, 0, -10, 0)) +
scale_fill_manual(values = aachen_color(c("maygreen", "green", "turquoise"))) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
top_overlap_genes
Time series cluster
profile_label = tribble(
~profile, ~process,
"STEM ID: 14", "Inflammation",
"STEM ID: 6", "Proliferation",
"STEM ID: 17", NA_character_,
"STEM ID: 12", NA_character_,
"STEM ID: 7", "Metabolism (1)",
"STEM ID: 9", "Metabolism (2)",
"STEM ID: 13", "ECM"
) %>%
mutate(profile = fct_inorder(profile),
process = coalesce(process, profile),
process = fct_inorder(process))
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "pure_ccl4") %>%
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)
# 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 = 2) +
labs(x = "Time in Months") +
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
Figure 1
fig1 <- (design + histology) /
((liver_enzymes | (gene_overlap / plot_spacer()) | top_overlap_genes) +
plot_layout(widths = c(1,2.5,2))) /
ts_cluster +
plot_layout(height = c(1, 1, 1)) +
plot_annotation(tag_levels = list(c("A", "B", "C", "D", "", "E", "F"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig1
ggsave(here("figures/Figure 1.pdf"), fig1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 1.png"), fig1,
width = 21, height = 29.7, units = c("cm")
)Partial Figure 1
Supplementary Figure 1.1
sfig1_1 <- pca_plot / (volcano | top_genes) +
plot_annotation(tag_levels = list(c("A", "B", "C"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig1_1
ggsave(here("figures/Supplementary Figure 1.1.pdf"), sfig1_1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 1.1.png"), sfig1_1,
width = 21, height = 29.7, units = c("cm")
)Time spend to execute this analysis: 00:55 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:
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#> other attached packages:
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#> [7] cowplot_1.1.0 AachenColorPalette_1.1.2 here_1.0.1
#> [10] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
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