Last updated: 2021-02-27

Checks: 7 0

Knit directory: liver-disease-atlas/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20201218) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 40203d5. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/human-diehl-nafld_cache/
    Ignored:    analysis/human-hampe13-nash_cache/
    Ignored:    analysis/human-hampe14-misc_cache/
    Ignored:    analysis/human-hoang-nafld_cache/
    Ignored:    analysis/human-ramnath-fibrosis_cache/
    Ignored:    analysis/meta-chronic-vs-acute_cache/
    Ignored:    analysis/meta-mouse-vs-human_cache/
    Ignored:    analysis/mouse-acute-apap_cache/
    Ignored:    analysis/mouse-acute-bdl_cache/
    Ignored:    analysis/mouse-acute-ccl4_cache/
    Ignored:    analysis/mouse-acute-lps_cache/
    Ignored:    analysis/mouse-acute-ph_cache/
    Ignored:    analysis/mouse-acute-tunicamycin_cache/
    Ignored:    analysis/mouse-chronic-ccl4_cache/
    Ignored:    analysis/plot-acute-apap_cache/
    Ignored:    analysis/plot-acute-bdl_cache/
    Ignored:    analysis/plot-acute-ccl4_cache/
    Ignored:    analysis/plot-acute-ph_cache/
    Ignored:    analysis/plot-chronic-ccl4_cache/
    Ignored:    analysis/plot-mouse-vs-human_cache/
    Ignored:    analysis/plot-precision-recall_cache/
    Ignored:    analysis/plot-study-overview_cache/
    Ignored:    analysis/plot-teufel-integration_cache/
    Ignored:    analysis/save-tables_cache/
    Ignored:    code/.DS_Store
    Ignored:    code/README.html
    Ignored:    code/meta-mouse-vs-human/.DS_Store
    Ignored:    data.zip
    Ignored:    data/.DS_Store
    Ignored:    data/annotation/
    Ignored:    data/human-diehl-nafld/
    Ignored:    data/human-hampe13-nash/
    Ignored:    data/human-hampe14-misc/
    Ignored:    data/human-hoang-nafld/
    Ignored:    data/human-ramnath-fibrosis/
    Ignored:    data/meta-chronic-vs-acute/
    Ignored:    data/meta-mouse-vs-human/
    Ignored:    data/mouse-acute-apap/
    Ignored:    data/mouse-acute-bdl/
    Ignored:    data/mouse-acute-ccl4/
    Ignored:    data/mouse-acute-lps/
    Ignored:    data/mouse-acute-ph/
    Ignored:    data/mouse-acute-tunicamycin/
    Ignored:    data/mouse-chronic-ccl4/
    Ignored:    external_software/.DS_Store
    Ignored:    external_software/README.html
    Ignored:    external_software/stem/.DS_Store
    Ignored:    figures/
    Ignored:    geo_submission/
    Ignored:    output/.DS_Store
    Ignored:    output/README.html
    Ignored:    output/human-diehl-nafld/
    Ignored:    output/human-hampe13-nash/
    Ignored:    output/human-hampe14-misc/
    Ignored:    output/human-hoang-nafld/
    Ignored:    output/human-ramnath-fibrosis/
    Ignored:    output/meta-chronic-vs-acute/
    Ignored:    output/meta-mouse-vs-human/
    Ignored:    output/mouse-acute-apap/
    Ignored:    output/mouse-acute-bdl/
    Ignored:    output/mouse-acute-ccl4/
    Ignored:    output/mouse-acute-lps/
    Ignored:    output/mouse-acute-ph/
    Ignored:    output/mouse-acute-tunicamycin/
    Ignored:    output/mouse-chronic-ccl4/
    Ignored:    renv/library/
    Ignored:    renv/staging/
    Ignored:    tables/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/plot-chronic-vs-acute.Rmd) and HTML (docs/plot-chronic-vs-acute.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html a92b255 christianholland 2021-02-27 Build site.
Rmd 424c861 christianholland 2021-02-27 minor change
html 9c62197 christianholland 2021-01-07 Build site.
html ce21ef0 christianholland 2021-01-05 Build site.
Rmd 41a051d christianholland 2021-01-05 polished figure
Rmd 0a6e6bc christianholland 2021-01-05 Build site.
Rmd ff46d59 christianholland 2020-12-29 Build site.
html bf1292e christianholland 2020-12-28 Build site.
Rmd 8918efd christianholland 2020-12-28 changed plot order
html 067c933 christianholland 2020-12-23 Build site.
Rmd d4f78fa christianholland 2020-12-23 wflow_publish("analysis/*.Rmd", delete_cache = T)
html 459dd00 christianholland 2020-12-22 Build site.
Rmd c85193d christianholland 2020-12-22 wflow_publish(c(“analysis/plot-chronic-vs-acute.Rmd”, “analysis/index.Rmd”),
html e693cef christianholland 2020-12-22 Build site.
Rmd 2cbfb36 christianholland 2020-12-22 added plottin scripts for fig 3

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

Version Author Date
a92b255 christianholland 2021-02-27
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

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

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 = "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))

Version Author Date
ce21ef0 christianholland 2021-01-05
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),
  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

Version Author Date
ce21ef0 christianholland 2021-01-05
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

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

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

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 = 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

Version Author Date
ce21ef0 christianholland 2021-01-05
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

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

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

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 = 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

Version Author Date
ce21ef0 christianholland 2021-01-05
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

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

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

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

Version Author Date
ce21ef0 christianholland 2021-01-05
bf1292e christianholland 2020-12-28
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

Version Author Date
a92b255 christianholland 2021-02-27
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

Version Author Date
ce21ef0 christianholland 2021-01-05
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

Version Author Date
ce21ef0 christianholland 2021-01-05
bf1292e christianholland 2020-12-28
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

Version Author Date
bf1292e christianholland 2020-12-28
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: 01:07 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