Last updated: 2020-12-23

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Knit directory: meta-liver/

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Rmd 7ca5d39 christianholland 2020-12-22 added plotting script for apap

Introduction

Here we generate publication ready plots of the analysis of the acute APAP mouse model.

Libraries and sources

These libraries and sources are used for this analysis.

library(tidyverse)
library(tidylog)
library(here)

library(AachenColorPalette)
library(cowplot)
library(lemon)
library(ggpubr)
library(patchwork)

source(here("code/utils-plots.R"))

Definition of global variables that are used throughout this analysis.

# i/o
data_path <- "data/mouse-acute-apap"
output_path <- "output/mouse-acute-apap"

# graphical parameters
# fontsize
fz <- 7

# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))

Design

design <- ggdraw() +
  draw_image(here(data_path, "exp-design.pdf"))

Histology

histology <- ggdraw() +
  draw_image(here(data_path, "histology.png"))

Liver enyzmes

df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
  mutate(time = fct_inorder(time)) %>%
  pivot_longer(col = -c(time, enzyme), names_to = "mouse", values_to = "y") %>%
  mutate(enzyme = factor(str_to_upper(enzyme), levels = c("ALT", "AST")))

df_summary <- df %>%
  group_by(time, enzyme) %>%
  summarise(mean_se(y)) %>%
  ungroup()

liver_enzymes <- df_summary %>%
  ggplot(aes(x = time, y = y)) +
  geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
  geom_col() +
  facet_rep_wrap(~enzyme, scales = "free", ncol = 1) +
  labs(x = NULL, y = "U/L") +
  my_theme(grid = "y", fsize = fz) +
  stat_compare_means(
    data = df, label = "p.signif",
    ref.group = "Control", hide.ns = T
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

liver_enzymes

Version Author Date
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PCA

pca_result <- readRDS(here(output_path, "pca_result.rds"))

keys <- key_mm %>%
  filter(treatment == "APAP" & class == "Acute") %>%
  distinct(time = value, label = time_label2) %>%
  drop_na() %>%
  add_row(time = 0, label = "Control") %>%
  mutate(time = ordered(time))

pca_plot <- pca_result$coords %>%
  arrange(time) %>%
  inner_join(keys, by = "time") %>%
  mutate(label = fct_inorder(label)) %>%
  ggplot(aes(x = PC1, y = PC2, color = label, label = label)) +
  geom_point() +
  labs(
    x = paste0("PC1", " (", pca_result$var[1], "%)"),
    y = paste0("PC2", " (", pca_result$var[2], "%)"),
    color = "Time"
  ) +
  my_theme(fsize = fz) +
  theme(legend.position = "top",
        legend.box.margin = margin(10, 0, -20, 0)) +
  scale_color_manual(values = aachen_color(c(
    "blue", "purple", "violet",
    "bordeaux", "red", "orange",
    "maygreen", "green", "turquoise",
    "petrol"
  )))

pca_plot

Version Author Date
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b5b58d0 christianholland 2020-12-22

Volcano plot

df <- readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "apap") %>%
  inner_join(key_mm, by = "contrast") %>%
  select(-contrast) %>%
  rename(contrast = time_label2) %>%
  mutate(contrast = fct_drop(contrast)) %>%
  mutate(regulation = fct_recode(regulation,
    Up = "up", Down = "down",
    n.s. = "ns"
  ))

deg_count <- df %>%
  add_count(contrast, regulation) %>%
  filter(regulation != "n.s.") %>%
  mutate(regulation = fct_drop(regulation)) %>%
  mutate(
    logFC = case_when(
      regulation == "Up" ~ 0.75 * max(logFC),
      regulation == "Down" ~ 0.75 * min(logFC)
    ),
    pval = 0.4
  ) %>%
  distinct(n, contrast, logFC, pval, regulation, value) %>%
  complete(contrast, nesting(regulation, logFC, pval), fill = list(n = 0))

main_time <- c(12, 24, 48)
# for main panel
volcano_main <- df %>%
  filter(value %in% main_time) %>%
  plot_volcano(nrow = 1) +
  geom_text(
    data = filter(deg_count, value %in% main_time),
    aes(y = pval, label = n), size = fz / (14 / 5),
    hjust = "inward", vjust = "inward",
    show.legend = F
  ) +
  theme(
    legend.position = "top",
    legend.box.margin = margin(-10, 0, -10, 0)
  ) +
  labs(color = "Regulation") +
  my_theme(grid = "y", fsize = fz)

# for supp panel
volcano_supp <- df %>%
  filter(!value %in% main_time) %>%
  plot_volcano(ncol = 1) +
  geom_text(
    data = filter(deg_count, !value %in% main_time),
    aes(y = pval, label = n), size = fz / (14 / 5),
    show.legend = F
  ) +
  theme(
    legend.position = "top",
    legend.box.margin = margin(-10, 0, -10, 0)
  ) +
  labs(color = "Regulation") +
  my_theme(grid = "y", fsize = fz)

volcano_main

Version Author Date
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volcano_supp

Version Author Date
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Top DEGs

df <- readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "apap") %>%
  inner_join(key_mm) %>%
  select(-class) %>%
  rename(class = time_label2)

top_genes_df <- df %>% 
  filter(regulation != "ns") %>%
  group_by(class, sign(logFC)) %>%
  slice_max(order_by = abs(logFC), n = 10, with_ties = F) %>%
  ungroup() %>% 
  nest(data = -c(class, value))

plots <- top_genes_df %>%
  mutate(p = pmap(., .f = plot_top_genes, fontsize = fz))

main_time <- c(12, 24, 48)

# for main panel
top_genes_main <- plots %>%
  filter(value %in% main_time) %>%
  pull(p) %>%
  wrap_plots() +
  plot_layout(nrow = 1)

# for supp panel
top_genes_supp <- plots %>%
  filter(!value %in% main_time) %>%
  pull(p) %>%
  wrap_plots() +
  plot_layout(ncol = 1)

top_genes_main

Version Author Date
b5b58d0 christianholland 2020-12-22
top_genes_supp

Version Author Date
b5b58d0 christianholland 2020-12-22

Time series cluster

profile_label <- tribble(
  ~profile, ~process,
  "STEM ID: 14", "Stress response",
  "STEM ID: 9", "Migration",
  "STEM ID: 12", "Proliferation",
  "STEM ID: 6", "Metabolism (1)",
  "STEM ID: 11", "Metabolism (2)",
  "STEM ID: 18", "Metabolism (3)",
  "STEM ID: 8", "Metabolism (4)"
) %>%
  mutate(
    profile = fct_inorder(profile),
    process = coalesce(process, profile),
    process = fct_inorder(process)
  )

stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
  filter(key == "apap") %>%
  filter(p <= 0.05) %>%
  mutate(profile = fct_reorder(str_c(
    "STEM ID: ",
    as.character(profile)
  ), p)) %>%
  inner_join(profile_label, by = "profile") %>%
  select(-profile) %>%
  rename(profile = process) %>%
  filter(!time %in% c(384, 192, 144))

# extract meta data of profiles
profile_anno <- stem_res %>%
  group_by(key, profile, p) %>%
  mutate(y = 1.2 * abs(max(value))) %>%
  ungroup() %>%
  mutate(max_time = max(time)) %>%
  distinct(key, profile, p, size, y, max_time) %>%
  mutate(label = str_c(size, " ", "genes"))

ts_cluster <- stem_res %>%
  plot_stem_profiles(model_profile = F, nrow = 1) +
  labs(x = "Time in Hours") +
  geom_text(
    data = profile_anno, aes(x = 0, y = y, label = label),
    inherit.aes = F, size = fz / (14 / 5), hjust = "inward"
  ) +
  my_theme(grid = "no", fsize = fz) +
  scale_x_continuous(
    breaks = unique(stem_res$time),
    guide = guide_axis(n.dodge = 3)
  )

ts_cluster

Version Author Date
b5b58d0 christianholland 2020-12-22

Collage

Figure 2

fig2 <- (design + histology) /
  ((liver_enzymes | pca_plot) + plot_layout(widths = c(1,3))) /
  ((volcano_main / top_genes_main)) /
  ts_cluster +
  plot_layout(height = c(1.5, 2, 1.75, 0.75)) +
  plot_annotation(tag_levels = list(c("A", "B", "C", "D", "E", "F", "", "", "",
                                      "", "", "G"))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

fig2

Version Author Date
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b5b58d0 christianholland 2020-12-22

ggsave(here("figures/Figure 2.pdf"), fig2,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 2.png"), fig2,
  width = 21, height = 29.7, units = c("cm")
)

Supplementary Figure 2.1

sfig2_1 <- (volcano_supp | top_genes_supp) +
  plot_annotation(tag_levels = list(c("A", "B"))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

sfig2_1

Version Author Date
6b6c16a christianholland 2020-12-23
b5b58d0 christianholland 2020-12-22

ggsave(here("figures/Supplementary Figure 2.1.pdf"), sfig2_1,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.1.png"), sfig2_1,
  width = 21, height = 29.7, units = c("cm")
)

Time spend to execute this analysis: 01:16 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
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#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
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#> 
#> 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] stats     graphics  grDevices datasets  utils     methods   base     
#> 
#> other attached packages:
#>  [1] patchwork_1.1.1          ggpubr_0.4.0             lemon_0.4.5             
#>  [4] cowplot_1.1.0            AachenColorPalette_1.1.2 here_1.0.1              
#>  [7] tidylog_1.0.2            forcats_0.5.0            stringr_1.4.0           
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#> [13] tidyr_1.1.2              tibble_3.0.4             ggplot2_3.3.2           
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