Chronic vs. acute (Fig. 3)
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 for the comparison of chronic and acute mouse models.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(AachenColorPalette)
library(VennDiagram)
library(scales)
library(lemon)
library(ComplexHeatmap)
library(ggwordcloud)
library(circlize)
library(patchwork)
source(here("code/utils-plots.R"))
source(here("code/utils-utils.R"))Definition of global variables and functions that are used throughout this analysis.
# i/o
data_path <- "data/meta-chronic-vs-acute"
output_path <- "output/meta-chronic-vs-acute"
# graphical parameters
# fontsize
fz <- 7
# color function for heatmaps
col_fun <- colorRamp2(
c(-4, 0, 4),
c(aachen_color("blue"), "white", aachen_color("red"))
)
# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))Time point of maximal liver damage
keys <- key_mm %>%
filter(class == "Acute" & treatment_abbr %in% c("CCl4", "APAP", "PH")) %>%
distinct(time = value, label = time_label2, treatment_abbr) %>%
drop_na() %>%
add_row(time = 0, label = "Control", treatment_abbr = "CCl4") %>%
add_row(time = 0, label = "Control", treatment_abbr = "APAP") %>%
add_row(time = 0, label = "Control", treatment_abbr = "PH") %>%
mutate(time = ordered(time))
pca_dist <- readRDS(here(output_path, "pca_dist.rds")) %>%
inner_join(keys, by = c("time", "treatment_abbr")) %>%
arrange(time) %>%
mutate(label = fct_inorder(label))
max_liver_damage <- pca_dist %>%
ggplot(aes(x = label, y = dist, fill = max)) +
geom_col() +
my_theme(grid = "y", fsize = fz) +
scale_fill_manual(values = aachen_color(c("black50", "green"))) +
labs(
x = NULL,
y = "Absolute mean distance to control along PC1"
) +
theme(
legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1)
) +
facet_rep_wrap(~treatment_abbr, scales = "free")
max_liver_damage
Similarity of acute gene sets
keys <- key_mm %>%
distinct(contrast, label = label2)
j <- readRDS(here(output_path, "gene_set_similarity.rds")) %>%
separate(set1, into = c("tmp1", "tmp2", "tmp3", "contrast"), sep = "-") %>%
select(-starts_with("tmp")) %>%
inner_join(keys, by = "contrast") %>%
rename(set1 = label) %>%
select(-contrast) %>%
separate(set2, into = c("tmp1", "tmp2", "tmp3", "contrast"), sep = "-") %>%
select(-starts_with("tmp")) %>%
inner_join(keys, by = "contrast") %>%
rename(set2 = label) %>%
select(-contrast)
acute_gs_sim <- j %>%
mutate(set1 = fct_rev(set1)) %>%
ggplot(aes(
x = set1, y = set2, fill = similarity,
label = round(similarity, 3)
)) +
geom_tile(color = "black", size = 0.2) +
scale_fill_gradient(low = "white", high = aachen_color("green")) +
labs(x = NULL, y = NULL, fill = "Jaccard\nIndex") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
geom_text(size = (fz - 2) / (14 / 5)) +
my_theme(grid = "no", fsize = fz)
acute_gs_sim
Interstudy enrichment of acute studies
keys <- key_mm %>%
distinct(contrast, label = label2)
gsea_res <- readRDS(here(output_path, "interstudy_enrichment.rds")) %>%
separate(signature,
into = c("tmp1", "tmp2", "tmp3", "contrast"),
sep = "-"
) %>%
select(-starts_with("tmp")) %>%
inner_join(keys, by = "contrast") %>%
rename(signature = label) %>%
select(-contrast) %>%
separate(geneset, into = c("tmp1", "tmp2", "tmp3", "contrast"), sep = "-") %>%
select(-starts_with("tmp")) %>%
inner_join(keys, by = "contrast") %>%
rename(geneset = label) %>%
select(-contrast)
acute_interstudy_enrichment <- gsea_res %>%
mutate(direction = fct_rev(str_to_title(direction))) %>%
mutate(label = gtools::stars.pval(padj)) %>%
ggplot(aes(x = signature, y = geneset, fill = ES)) +
geom_tile() +
geom_text(aes(label = label), size = fz / (14 / 5), vjust = 1) +
facet_wrap(~direction, ncol = 1) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
scale_fill_gradient2(
low = aachen_color("blue"), mid = "white",
high = aachen_color("red")
) +
my_theme(grid = "no", fsize = fz) +
labs(x = "Signature", y = "Gene Set", fill = "ES") +
guides(fill = guide_colorbar(title = "ES"))
acute_interstudy_enrichment
Union of acute and chronic genes
keys <- key_mm %>%
distinct(contrast, label = label2)
c <- readRDS(here(output_path, "chronic_gene_pool.rds"))
a <- readRDS(here(output_path, "acute_gene_pool.rds"))
df <- bind_rows(c, a) %>%
inner_join(keys, by = "contrast") %>%
mutate(
statistic = case_when(
statistic >= 25 ~ 25,
TRUE ~ statistic
),
class = str_to_title(class)
)
union_a <- df %>%
filter(class == "Acute") %>%
ggplot(aes(x = label, y = fct_reorder(gene, statistic, mean))) +
geom_tile(aes(fill = statistic)) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none"
) +
labs(y = "Pool of acute genes", x = NULL, fill = "t-statistic") +
facet_rep_wrap(~class) +
scale_fill_gradient2(
low = aachen_color("blue"), mid = "white",
high = aachen_color("red")
) +
my_theme(grid = "no", fsize = fz)
union_c <- df %>%
filter(class == "Chronic") %>%
ggplot(aes(
x = label, y = fct_reorder(gene, statistic, mean),
fill = statistic
)) +
geom_tile() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
) +
labs(y = "Pool of chronic genes", x = NULL, fill = "t-statistic") +
facet_rep_wrap(~class) +
scale_fill_gradient2(
low = aachen_color("blue"), mid = "white",
high = aachen_color("red")
) +
my_theme(grid = "no", fsize = fz)
union_a +
union_c +
plot_layout(widths = c(8, 1))
Overlap of unified gene sets
acute_gene_union <- readRDS(here(output_path, "union_acute_geneset.rds")) %>%
mutate(class = "acute")
chronic_gene_union <- readRDS(here(
output_path,
"union_chronic_geneset.rds"
)) %>%
mutate(class = "chronic")
a1 <- acute_gene_union %>% nrow()
a2 <- chronic_gene_union %>% nrow()
ca <- intersect(
acute_gene_union %>% pull(gene),
chronic_gene_union %>% pull(gene)
) %>%
length()
grid.newpage()
v <- grid.grabExpr(draw.pairwise.venn(
area1 = a1, area2 = a2, cross.area = ca,
category = c("Acute", "Chronic"),
# lty = "blank",
cex = 1 / 12 * fz,
fontfamily = rep("sans", 3),
# fill = aachen_color(c("purple", "petrol")),
# cat.col = aachen_color(c("purple", "petrol")),
cat.cex = 1 / 12 * (fz + 1),
cat.fontfamily = rep("sans", 2),
cat.pos = c(340, 20),
cat.just = list(c(0.5, 0), c(0.5, 0))
))Top exclusive chronic genes
Heatmap
keys <- key_mm %>%
distinct(contrast, label2)
df <- readRDS(here(output_path, "ranked_exclusive_chronic_genes.rds"))
contrasts <- readRDS(here(output_path, "limma_result.rds"))
acute_contrasts <- c(
"treat_vs_ctrl",
"inLiver_lps_vs_ctrl",
"ccl_8h_vs_0h", "ccl_24h_vs_0h", "ccl_48h_vs_0h",
"apap_12h_vs_0h", "apap_24h_vs_0h", "apap_48h_vs_0h",
"ph_0.5d", "ph_1d", "ph_2d",
"bdl_vs_sham_1d"
)
mat_exclusive_chronic_genes <- df %>%
filter(rank <= 100) %>%
left_join(contrasts) %>%
filter(contrast %in% acute_contrasts | treatment == "pure_ccl4") %>%
inner_join(keys, by = "contrast") %>%
mutate(gene = as_factor(gene)) %>%
select(gene, label2, logFC) %>%
untdy("gene", "label2", "logFC") %>%
as.matrix()
exclusive_chronic_hmap <- Heatmap(
t(mat_exclusive_chronic_genes),
col = col_fun,
cluster_rows = T, cluster_columns = T,
show_row_dend = F,
row_names_gp = gpar(fontsize = fz), column_names_gp = gpar(fontsize = fz - 2),
name = "logFC",
heatmap_legend_param = list(
title_gp = gpar(
fontface = "plain",
fontsize = fz + 1
),
labels_gp = gpar(fontsize = fz)
),
row_gap = unit(2.5, "mm"),
border = T,
row_split = c(rep("Acute", 12), rep("Chronic", 3)),
row_title_gp = gpar(fontsize = fz + 1)
)
exclusive_chronic_hmap
Characteriatzion via TFs
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("dorothea") & fdr <= 0.2) %>%
filter(class == "chronic") %>%
mutate(regulation = str_to_title(regulation))
chronic_tfs_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("Hif1a", "Klf5")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
chronic_tfs_down <- ora_res %>%
filter(regulation == "Down") %>%
slice_min(order_by = p.value, n = 3) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
chronic_tfs_up + chronic_tfs_down
Characteriatzion via pathways
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("progeny") & fdr <= 0.2) %>%
filter(class == "chronic") %>%
mutate(regulation = str_to_title(regulation))
chronic_pw_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("TGFb")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
chronic_pw_up
Characterization via GO terms
# get wordcounts for go terms
wordcounts <- readRDS(here(output_path, "go_wordcounts.rds")) %>%
filter(class == "chronic")
# get ranking of go clusters
cluster_ranking <- readRDS(here(output_path, "go_cluster_ranking.rds")) %>%
filter(class == "chronic") %>%
group_by(cluster, class) %>%
mutate(label = str_c(n(), " ", "GO terms")) %>%
ungroup() %>%
mutate(regulation = fct_rev(str_to_title(regulation))) %>%
arrange(desc(cluster)) %>%
mutate(description = fct_rev(as_factor(description)))
cluster_anno <- cluster_ranking %>%
nest(data = c(rank, term)) %>%
# find max peak for each cluster
mutate(peak = data %>% map(function(data) {
max(density(data$rank)$y)
})) %>%
unnest(c(peak)) %>%
group_by(regulation) %>%
mutate(max_peak = max(peak)) %>%
ungroup() %>%
distinct(
cluster, regulation, description, peak, max_peak, max_rank,
label
) %>%
arrange(description) %>%
group_by(regulation) %>%
mutate(n_clusters = row_number()) %>%
mutate(x_coord = case_when(
n_clusters == 1 ~ 0,
n_clusters == 2 ~ 1 * max_rank
)) %>%
ungroup()
# up-regulated genes
up_dens <- cluster_ranking %>%
filter(regulation == "Up") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Up"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color(c("petrol", "orange"))) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
up_cloud <- wordcounts %>%
filter(regulation == "up") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
exclusive_chronic_up <- up_dens +
inset_element(up_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
exclusive_chronic_up
Top common genes
Heatmap
keys <- key_mm %>%
distinct(contrast, label2)
df <- readRDS(here(output_path, "ranked_common_genes.rds"))
contrasts <- readRDS(here(output_path, "limma_result.rds"))
acute_contrasts <- c(
"treat_vs_ctrl",
"inLiver_lps_vs_ctrl",
"ccl_8h_vs_0h", "ccl_24h_vs_0h", "ccl_48h_vs_0h",
"apap_12h_vs_0h", "apap_24h_vs_0h", "apap_48h_vs_0h",
"ph_0.5d", "ph_1d", "ph_2d",
"bdl_vs_sham_1d"
)
mat_common_genes <- df %>%
filter(rank <= 100) %>%
left_join(contrasts) %>%
filter(contrast %in% acute_contrasts | treatment == "pure_ccl4") %>%
inner_join(keys, by = "contrast") %>%
mutate(gene = as_factor(gene)) %>%
select(gene, label2, logFC) %>%
untdy("gene", "label2", "logFC") %>%
as.matrix()
common_hmap <- Heatmap(
t(mat_common_genes),
col = col_fun,
cluster_rows = T, cluster_columns = T,
show_row_dend = F,
row_names_gp = gpar(fontsize = fz), column_names_gp = gpar(fontsize = fz - 2),
name = "logFC",
heatmap_legend_param = list(
title_gp = gpar(
fontface = "plain",
fontsize = fz + 1
),
labels_gp = gpar(fontsize = fz)
),
row_gap = unit(2.5, "mm"),
border = T,
row_split = c(rep("Acute", 12), rep("Chronic", 3)),
row_title_gp = gpar(fontsize = fz + 1)
)
common_hmap
Characteriatzion via TFs
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("dorothea") & fdr <= 0.2) %>%
filter(class == "common") %>%
mutate(regulation = str_to_title(regulation))
common_tfs_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("Klf5")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
common_tfs_down <- ora_res %>%
filter(regulation == "Down") %>%
slice_min(order_by = p.value, n = 3) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
common_tfs_up + common_tfs_down
Characteriatzion via pathways
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("progeny") & fdr <= 0.2) %>%
filter(class == "common") %>%
mutate(regulation = str_to_title(regulation))
common_pw_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("NFkB", "TNFa")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
# no common_pw_up
common_pw_up
Characterization via GO terms
# get wordcounts for go terms
wordcounts <- readRDS(here(output_path, "go_wordcounts.rds")) %>%
filter(class == "common")
# get ranking of go clusters
cluster_ranking <- readRDS(here(output_path, "go_cluster_ranking.rds")) %>%
filter(class == "common") %>%
group_by(cluster, class) %>%
mutate(label = str_c(n(), " ", "GO terms")) %>%
ungroup() %>%
mutate(regulation = fct_rev(str_to_title(regulation))) %>%
arrange(desc(cluster)) %>%
mutate(description = fct_rev(as_factor(description)))
cluster_anno <- cluster_ranking %>%
nest(data = c(rank, term)) %>%
# find max peak for each cluster
mutate(peak = data %>% map(function(data) {
max(density(data$rank)$y)
})) %>%
unnest(c(peak)) %>%
group_by(regulation) %>%
mutate(max_peak = max(peak)) %>%
ungroup() %>%
distinct(
cluster, regulation, description, peak, max_peak, max_rank,
label
) %>%
arrange(description) %>%
group_by(regulation) %>%
mutate(n_clusters = row_number()) %>%
mutate(x_coord = case_when(
n_clusters == 1 ~ 0,
n_clusters == 2 ~ 1 * max_rank
)) %>%
ungroup()
# up-regulated genes
up_dens <- cluster_ranking %>%
filter(regulation == "Up") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Up"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color("turquoise")) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
up_cloud <- wordcounts %>%
filter(regulation == "up") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
common_up <- up_dens +
inset_element(up_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
# down-regulated genes
down_dens <- cluster_ranking %>%
filter(regulation == "Down") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Down"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color("purple")) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
down_cloud <- wordcounts %>%
filter(regulation == "down") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
common_down <- down_dens +
inset_element(down_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
common_up + common_down
Top exclusive acute genes
Heatmap
keys <- key_mm %>%
distinct(contrast, label2)
df <- readRDS(here(output_path, "ranked_exclusive_acute_genes.rds"))
contrasts <- readRDS(here(output_path, "limma_result.rds"))
acute_contrasts <- c(
"treat_vs_ctrl",
"inLiver_lps_vs_ctrl",
"ccl_8h_vs_0h", "ccl_24h_vs_0h", "ccl_48h_vs_0h",
"apap_12h_vs_0h", "apap_24h_vs_0h", "apap_48h_vs_0h",
"ph_0.5d", "ph_1d", "ph_2d",
"bdl_vs_sham_1d"
)
mat_exclusive_acute_genes <- df %>%
filter(rank <= 100) %>%
left_join(contrasts) %>%
filter(contrast %in% acute_contrasts | treatment == "pure_ccl4") %>%
inner_join(keys, by = "contrast") %>%
mutate(gene = as_factor(gene)) %>%
select(gene, label2, logFC) %>%
untdy("gene", "label2", "logFC") %>%
as.matrix()
exclusive_acute_hmap <- Heatmap(
t(mat_exclusive_acute_genes),
col = col_fun,
cluster_rows = T, cluster_columns = T,
show_row_dend = F,
row_names_gp = gpar(fontsize = fz), column_names_gp = gpar(fontsize = fz - 2),
name = "logFC",
heatmap_legend_param = list(
title_gp = gpar(
fontface = "plain",
fontsize = fz + 1
),
labels_gp = gpar(fontsize = fz)
),
row_gap = unit(2.5, "mm"),
border = T,
row_split = c(rep("Acute", 12), rep("Chronic", 3)),
row_title_gp = gpar(fontsize = fz + 1)
)
exclusive_acute_hmap
Characteriatzion via TFs
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("dorothea") & fdr <= 0.2) %>%
filter(class == "acute") %>%
mutate(regulation = str_to_title(regulation))
acute_tfs_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("Myc", "Trp53", "Stat3")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
acute_tfs_down <- ora_res %>%
filter(regulation == "Down") %>%
slice_min(order_by = p.value, n = 3) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
acute_tfs_up + acute_tfs_down
Characteriatzion via pathways
ora_res <- readRDS(here(
output_path,
"exclusive_genes_characterization.rds"
)) %>%
filter(group %in% c("progeny") & fdr <= 0.2) %>%
filter(class == "acute") %>%
mutate(regulation = str_to_title(regulation))
acute_pw_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("MAPK", "EGFR", "TNFa")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
acute_pw_down <- ora_res %>%
filter(regulation == "Down") %>%
slice_min(order_by = p.value, n = 3) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
acute_pw_up + acute_pw_down
Characterization via GO terms
# get wordcounts for go terms
wordcounts <- readRDS(here(output_path, "go_wordcounts.rds")) %>%
filter(class == "acute")
# get ranking of go clusters
cluster_ranking <- readRDS(here(output_path, "go_cluster_ranking.rds")) %>%
filter(class == "acute") %>%
group_by(cluster, class) %>%
mutate(label = str_c(n(), " ", "GO terms")) %>%
ungroup() %>%
mutate(regulation = fct_rev(str_to_title(regulation))) %>%
arrange(desc(cluster)) %>%
mutate(description = fct_rev(as_factor(description)))
cluster_anno <- cluster_ranking %>%
nest(data = c(rank, term)) %>%
# find max peak for each cluster
mutate(peak = data %>% map(function(data) {
max(density(data$rank)$y)
})) %>%
unnest(c(peak)) %>%
group_by(regulation) %>%
mutate(max_peak = max(peak)) %>%
ungroup() %>%
distinct(
cluster, regulation, description, peak, max_peak, max_rank,
label
) %>%
arrange(description) %>%
group_by(regulation) %>%
mutate(n_clusters = row_number()) %>%
mutate(x_coord = case_when(
n_clusters == 1 ~ 0,
n_clusters == 2 ~ 1 * max_rank
)) %>%
ungroup()
# up-regulated genes
up_dens <- cluster_ranking %>%
filter(regulation == "Up") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Up"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color(c("blue", "maygreen"))) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
up_cloud <- wordcounts %>%
filter(regulation == "up") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
exclusive_acute_up <- up_dens +
inset_element(up_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
# down-regulated genes
down_dens <- cluster_ranking %>%
filter(regulation == "Down") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Down"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color("purple")) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
down_cloud <- wordcounts %>%
filter(regulation == "down") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
exclusive_acute_down <- down_dens +
inset_element(down_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
exclusive_acute_up + exclusive_acute_down
Collage
Figure 3
Main Figure.
fig3 <- (((acute_gs_sim / v) + plot_layout(height = c(4, 1))) |
acute_interstudy_enrichment) /
grid.grabExpr(draw(exclusive_chronic_hmap)) /
((chronic_tfs_up | chronic_pw_up | exclusive_chronic_up | plot_spacer()) +
plot_layout(width = c(1, 0.5, 2, 1))) /
grid.grabExpr(draw(common_hmap)) /
((common_tfs_up | common_pw_up | common_up | plot_spacer()) +
plot_layout(width = c(0.5, 1, 2, 1))) +
plot_layout(height = c(1.5, 1.75, 0.75, 1.75, 0.75)) +
plot_annotation(tag_levels = list(c(
"A", "C", "B", "D", "E", "F", "G", "", "H", "I", "J", "K"
))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig3
ggsave(here("figures/Figure 3.pdf"), fig3,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 3.png"), fig3,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 3.1
Plot to determine the time point of maximal liver damage.
sfig3_1 <- max_liver_damage +
theme(
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig3_1
ggsave(here("figures/Supplementary Figure 3.1.pdf"), sfig3_1,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 3.1.png"), sfig3_1,
width = 21, height = 10, units = c("cm")
)Supplementary Figure 3.2
Direction of regulation for union of differential expressed genes for acute and chronic.
sfig3_2 <- union_a + union_c +
plot_layout(widths = c(8, 1)) +
theme(
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig3_2
ggsave(here("figures/Supplementary Figure 3.2.pdf"), sfig3_2,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 3.2.png"), sfig3_2,
width = 21, height = 10, units = c("cm")
)Supplementary Figure 3.3
Heatmap of exclusive acute genes and characterization of all up- and down regulated exclusive acute genes.
sfig3_3 <- plot_spacer() /
grid.grabExpr(draw(exclusive_acute_hmap)) /
((acute_tfs_up | acute_pw_up | exclusive_acute_up) +
plot_layout(width = c(0.5, 0.5, 2))) /
((acute_tfs_down | acute_pw_down | exclusive_acute_down) +
plot_layout(width = c(0.5, 0.5, 2))) +
plot_layout(height = c(0, 2, 1, 1)) +
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")
)
sfig3_3
ggsave(here("figures/Supplementary Figure 3.3.pdf"), sfig3_3,
width = 21, height = 20, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 3.3.png"), sfig3_3,
width = 21, height = 20, units = c("cm")
)Supplementary Figure 3.4
Characterization of down-regulated common genes.
sfig3_4 <- (common_tfs_down | common_down) +
plot_layout(width = c(0.5, 2)) +
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")
)
sfig3_4
ggsave(here("figures/Supplementary Figure 3.4.pdf"), sfig3_4,
width = 21, height = 7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 3.4.png"), sfig3_4,
width = 21, height = 7, units = c("cm")
)Time spend to execute this analysis: 01:01 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] grid stats graphics grDevices datasets utils methods
#> [8] base
#>
#> other attached packages:
#> [1] patchwork_1.1.1 circlize_0.4.11 ggwordcloud_0.5.0
#> [4] ComplexHeatmap_2.4.3 lemon_0.4.5 scales_1.1.1
#> [7] VennDiagram_1.6.20 futile.logger_1.4.3 AachenColorPalette_1.1.2
#> [10] here_1.0.1 tidylog_1.0.2 forcats_0.5.0
#> [13] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
#> [16] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
#> [19] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] fs_1.5.0 lubridate_1.7.9.2 RColorBrewer_1.1-2
#> [4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.2
#> [7] backports_1.2.1 R6_2.5.0 DBI_1.1.0
#> [10] colorspace_2.0-0 GetoptLong_1.0.5 withr_2.3.0
#> [13] tidyselect_1.1.0 gridExtra_2.3 compiler_4.0.2
#> [16] git2r_0.27.1 cli_2.2.0 rvest_0.3.6
#> [19] formatR_1.7 xml2_1.3.2 labeling_0.4.2
#> [22] digest_0.6.27 rmarkdown_2.6 pkgconfig_2.0.3
#> [25] htmltools_0.5.0 dbplyr_2.0.0 rlang_0.4.9
#> [28] GlobalOptions_0.1.2 readxl_1.3.1 rstudioapi_0.13
#> [31] farver_2.0.3 shape_1.4.5 generics_0.1.0
#> [34] jsonlite_1.7.2 gtools_3.8.2 magrittr_2.0.1
#> [37] Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
#> [40] lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
#> [43] yaml_2.2.1 plyr_1.8.6 parallel_4.0.2
#> [46] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
#> [49] cowplot_1.1.0 haven_2.3.1 hms_0.5.3
#> [52] knitr_1.30 pillar_1.4.7 rjson_0.2.20
#> [55] codetools_0.2-16 clisymbols_1.2.0 futile.options_1.0.1
#> [58] reprex_0.3.0 glue_1.4.2 evaluate_0.14
#> [61] lambda.r_1.2.4 renv_0.12.3 modelr_0.1.8
#> [64] png_0.1-7 vctrs_0.3.6 httpuv_1.5.4
#> [67] cellranger_1.1.0 gtable_0.3.0 clue_0.3-58
#> [70] assertthat_0.2.1 xfun_0.19 broom_0.7.3
#> [73] later_1.1.0.1 cluster_2.1.0 ellipsis_0.3.1