Chronic vs. acute (Fig. 3)

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Last updated: 2020-12-28

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Knit directory: meta-liver/

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File	Version	Author	Date	Message
Rmd	8918efd	christianholland	2020-12-28	changed plot order
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Rmd	d4f78fa	christianholland	2020-12-23	wflow_publish("analysis/*.Rmd", delete_cache = T)
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html	e693cef	christianholland	2020-12-22	Build site.
Rmd	2cbfb36	christianholland	2020-12-22	added plottin scripts for fig 3

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Introduction

Here we generate plots display various features of all included studies.

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 = "Time in hours",
    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

Past versions of time-point-max-liver-damage-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of sim-acute-gene-sets-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of interstudy-enrichment-acute-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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 = "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 = "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))

Past versions of union-chronic-acute-gene-pools-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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)
))

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 = F, cluster_columns = T,
  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

Past versions of exclusive-chronic-hmap-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-chronic-tfs-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-chronic-pws-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-chronic-go-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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 = F, cluster_columns = T,
  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

Past versions of common-hmap-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of common-tfs-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of common-pws-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of common-go-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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 = F, cluster_columns = T,
  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

Past versions of exclusive-acute-hmap-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-acute-tfs-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-acute-pws-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of exclusive-acute-go-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of fig-3-1.png

Version	Author	Date
459dd00	christianholland	2020-12-22

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

Past versions of s-fig-3-1-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of s-fig-3-2-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of s-fig-3-3-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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

Past versions of s-fig-3-4-1.png

Version	Author	Date
e693cef	christianholland	2020-12-22

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: 00:56 minutes.

Session information

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