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Knit directory: liver-disease-atlas/

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Introduction

Here we generate publication-ready plots for the integration of further chronic mouse models.

Libraries and sources

These libraries and sources are used for this analysis.

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

library(AachenColorPalette)
library(scales)
library(UpSetR)
library(grid)
library(lemon)
library(ggrepel)
library(magick)
library(patchwork)

library(gtools)

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

Definition of global variables and functions that are used throughout this analysis.

# i/o
data_path <- "data/meta-mouse-vs-human"
output_path <- "output/meta-mouse-vs-human"

# graphical parameters
# fontsize
fz <- 9

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

Chronic mouse model genes in number

keys <- key_mm %>%
  distinct(study = contrast, label2)

df <- readRDS(here(output_path, "chronic_mouse_deg_numbers.rds")) %>%
  left_join(keys, by = "study") %>%
  mutate(label2 = coalesce(label2, study)) %>%
  mutate(regulation = fct_rev(str_to_title(regulation)))

num_genes <- df %>%
  ggplot(aes(y = fct_reorder(label2, n, sum), x = n, fill = regulation)) +
  geom_col(position = "dodge") +
  labs(
    x = "Number of differentially expressed genes",
    y = "Chronic mouse model",
    fill = "Regulation"
  ) +
  scale_x_log10(
    breaks = trans_breaks("log10", function(x) 10^x),
    labels = trans_format("log10", scales::math_format(10^.x))
  ) +
  my_theme(grid = "x", fsize = fz) +
  scale_fill_manual(values = aachen_color(c("red75", "blue75"))) +
  annotation_logticks(sides = "b")

num_genes

Version Author Date
1f624c7 christianholland 2021-02-27
c2af37f christianholland 2020-12-29

Similarity of chronic mouse models and patient cohorts

contrast_keys_human <- key_hs %>%
  distinct(contrast, label, source, phenotype) %>%
  unite(contrast, source, phenotype, contrast, sep = "-")

contrast_keys_mouse <- key_mm %>%
  filter(str_detect(contrast, "pure")) %>%
  distinct(contrast, label)

keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)


o <- readRDS(here(output_path, "cross_species_similarity.rds"))

oo <- o %>%
  rename(contrast = set1) %>%
  left_join(keys, by = "contrast") %>%
  mutate(label = coalesce(label, contrast)) %>%
  rename(set1 = label, contrast = set2, contrast_set1 = contrast) %>%
  left_join(keys, by = "contrast") %>%
  mutate(label = coalesce(label, contrast)) %>%
  rename(set2 = label, contrast_set2 = contrast) %>%
  mutate(
    contrast_set1 = factor(contrast_set1, levels = levels(o$set1)),
    contrast_set2 = factor(contrast_set2, levels = levels(o$set2)),
    set1 = fct_reorder(set1, as.numeric(contrast_set1)),
    set2 = fct_reorder(set2, as.numeric(contrast_set2))
  ) %>%
  select(set1, set2, similarity)

gs_sim <- oo %>%
  filter(!str_detect(set1, "NAFLD|NASH|PSC|PBC|HCV")) %>%
  filter(!str_detect(set2, "WTD|HF|PTEN|MCD|STZ|CCl4")) %>%
  ggplot(aes(
    x = set1, y = set2, fill = similarity,
    label = round(similarity, 3)
  )) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = aachen_color("green")) +
  labs(x = NULL, y = NULL, fill = "Overlap\ncoefficient") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.line = element_blank(),
    axis.ticks = element_blank()
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  my_theme(fsize = fz, grid = "no")

gs_sim

Version Author Date
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c2af37f christianholland 2020-12-29

Enrichment of chronic mouse genes in patient cohorts

contrast_keys_human <- key_hs %>%
  distinct(contrast, label, source) %>%
  unite(contrast, source, contrast)

contrast_keys_mouse <- key_mm %>%
  filter(str_detect(contrast, "pure")) %>%
  distinct(contrast, label)

keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)

gsea_res <- readRDS(here(output_path, "cross_species_enrichment.rds")) %>%
  rename(contrast = signature) %>%
  inner_join(keys, by = "contrast") %>%
  select(-contrast) %>%
  rename(signature = label, contrast = geneset) %>%
  left_join(keys, by = "contrast") %>%
  mutate(label = coalesce(label, contrast)) %>%
  select(-contrast) %>%
  rename(geneset = label) %>%
  mutate(direction = fct_rev(str_to_title(direction)))

tile_up <- gsea_res %>%
  filter(direction == "Up") %>%
  mutate(
    label = stars.pval(padj),
    direction = fct_rev(direction)
  ) %>%
  ggplot(aes(x = signature, y = geneset, fill = ES)) +
  geom_tile() +
  geom_text(aes(label = label)) +
  facet_rep_wrap(~direction, scales = "free", ncol = 1) +
  my_theme(grid = "no", fsize = fz) +
  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"), limits = c(-1, 1)
  ) +
  my_theme(grid = "no", fsize = fz) +
  labs(x = "Signature", y = "Gene Set", fill = "ES") +
  guides(fill = guide_colorbar(title = "ES"))

box_up <- gsea_res %>%
  filter(direction == "Up") %>%
  ggplot(aes(y = geneset, x = ES)) +
  geom_boxplot() +
  facet_rep_wrap(~direction, ncol = 1) +
  geom_vline(xintercept = 0) +
  my_theme(fsize = fz, grid = "x") +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.line.y = element_blank(),
    axis.ticks.y = element_blank()
  )

tile_down <- gsea_res %>%
  filter(direction == "Down") %>%
  mutate(
    label = stars.pval(padj),
    direction = fct_rev(direction)
  ) %>%
  ggplot(aes(x = signature, y = geneset, fill = ES)) +
  geom_tile() +
  geom_text(aes(label = label)) +
  facet_rep_wrap(~direction, scales = "free", ncol = 1) +
  my_theme(grid = "no", fsize = fz) +
  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"), limits = c(-1, 1)
  ) +
  my_theme(grid = "no", fsize = fz) +
  labs(x = "Signature", y = "Gene Set", fill = "ES") +
  guides(fill = guide_colorbar(title = "ES"))

box_down <- gsea_res %>%
  filter(direction == "Down") %>%
  ggplot(aes(y = geneset, x = ES)) +
  geom_boxplot() +
  facet_rep_wrap(~direction, ncol = 1) +
  geom_vline(xintercept = 0) +
  my_theme(fsize = fz, grid = "x") +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.line.y = element_blank(),
    axis.ticks.y = element_blank()
  )

tile_up + box_up

Version Author Date
1f624c7 christianholland 2021-02-27
c2af37f christianholland 2020-12-29
tile_down + box_down

Version Author Date
1f624c7 christianholland 2021-02-27
c2af37f christianholland 2020-12-29

Etiology genes in numbers

df <- readRDS(here(output_path, "etiology_gene_sets.rds"))

mat_up <- df %>%
  filter(regulation == "up") %>%
  select(-regulation) %>%
  mutate(val = 1) %>%
  spread(etiology, val, fill = 0) %>%
  data.frame(row.names = 1)

pdf(file = here("figures/tmp/Fig5A1.pdf"), width = 15, height = 10, onefile = F)
upset(mat_up,
  nintersects = NA, mainbar.y.label = "Common genes",
  sets.x.label = "Total number of genes",
  text.scale = 3, point.size = 5
)
grid.text("Up", x = 0.65, y = 0.95, gp = gpar(fontsize = fz * 4))
dev.off()
#> quartz_off_screen 
#>                 2

mat_down <- df %>%
  filter(regulation == "down") %>%
  select(-regulation) %>%
  mutate(val = 1) %>%
  spread(etiology, val, fill = 0) %>%
  data.frame(row.names = 1)

pdf(file = here("figures/tmp/Fig5A2.pdf"), width = 15, height = 10, onefile = F)
upset(mat_down,
  nintersects = NA, mainbar.y.label = "Common genes",
  sets.x.label = "Total number of genes",
  text.scale = 3, point.size = 5
)
grid.text("Down", x = 0.65, y = 0.95, gp = gpar(fontsize = fz * 4))
dev.off()
#> quartz_off_screen 
#>                 2

PR scatter plot of chronic mouse models

keys <- key_mm %>%
  distinct(study = contrast, label2)

pr <- readRDS(here(output_path, "precision_recall.rds")) %>%
  left_join(keys, by = "study") %>%
  mutate(label2 = coalesce(label2, study)) %>%
  mutate(regulation = fct_rev(str_to_title(regulation))) %>%
  mutate(x = case_when(
    str_detect(label2, "12 Month") ~ as.character(label2),
    TRUE ~ NA_character_
  )) %>%
  mutate(etiology = factor(etiology, levels = c(
    "NAFLD", "NASH", "HCV", "PSC",
    "PBC"
  ))) %>%
  mutate(label2 = case_when(
    str_detect(label2, "HF12") ~ "High-fat diet (12 Weeks)",
    str_detect(label2, "HF18") ~ "High-fat diet (18 Weeks)",
    str_detect(label2, "HF30") ~ "High-fat diet (30 Weeks)",
    str_detect(label2, "STZ12") ~ "Streptozocin diet (12 Weeks)",
    str_detect(label2, "STZ18") ~ "Streptozocin diet (18 Weeks)",
    str_detect(label2, "MCD4") ~ "Methionine- and choline-deficient diet (4 Weeks)",
    str_detect(label2, "MCD8") ~ "Methionine- and choline-deficient diet (8 Weeks)",
    str_detect(label2, "WTD") ~ "Western-type diet (12 Weeks)",
    str_detect(label2, "PTEN") ~ "PTEN knockout mice",
    TRUE ~ as.character(label2)
  )) %>%
  mutate(label2 = factor(label2, levels = c(
    "CCl4 (2 Months)", "CCl4 (6 Months)", "CCl4 (12 Months)",
    "High-fat diet (12 Weeks)", "High-fat diet (18 Weeks)",
    "High-fat diet (30 Weeks)", "Streptozocin diet (12 Weeks)",
    "Streptozocin diet (18 Weeks)",
    "Methionine- and choline-deficient diet (4 Weeks)",
    "Methionine- and choline-deficient diet (8 Weeks)",
    "Western-type diet (12 Weeks)",
    "PTEN knockout mice"
  )))

pr_plot_mm <- pr %>%
  ggplot(aes(x = recall, y = precision, label = x, color = label2)) +
  geom_point() +
  facet_rep_grid(regulation ~ etiology) +
  geom_text_repel(size = fz / (14 / 5), show.legend = FALSE, na.rm = TRUE) +
  geom_abline(lty = "dashed") +
  expand_limits(x = 0, y = 0) +
  labs(x = "Recall", y = "Precision", color = "Mouse model") +
  my_theme(fsize = fz) +
  scale_color_viridis_d(direction = -1, option = "B") +
  theme(legend.position = "top") +
  guides(col = guide_legend(nrow = 4))

pr_plot_mm

Version Author Date
1f624c7 christianholland 2021-02-27

PR scatter plot of chronicity categories

pr <- readRDS(here(output_path, "precision_recall_chronicity.rds")) %>%
  mutate(
    etiology = factor(etiology, levels = c("NAFLD", "NASH", "HCV", "PSC", "PBC")),
    regulation = factor(str_to_title(regulation), levels = c("Up", "Down")),
    class = case_when(
      str_detect(class, "acute") ~ "Exclusive acute",
      str_detect(class, "chronic") ~ "Exclusive chronic",
      str_detect(class, "common") ~ "Common in chronic and acute"
    ),
    class = factor(class, levels = c(
      "Exclusive chronic",
      "Exclusive acute",
      "Common in chronic and acute"
    ))
  )

set.seed(123)
pr_plot_category <- pr %>%
  ggplot(aes(
    x = recall, y = precision, label = class,
    color = class
  )) +
  geom_jitter(width = 0.0015, height = 0) +
  facet_rep_grid(regulation ~ etiology) +
  geom_abline(lty = "dashed") +
  expand_limits(x = 0, y = 0) +
  labs(x = "Recall", y = "Precision", color = "Category") +
  my_theme(fsize = fz) +
  scale_color_manual(values = aachen_color(c("bordeaux", "orange", "petrol"))) +
  theme(legend.position = "top")

pr_plot_category

Version Author Date
1f624c7 christianholland 2021-02-27

Collage

Figure 5

Main Figure.

upset_up <- image_ggplot(image_read_pdf(here("figures/tmp/Fig5A1.pdf")),
  interpolate = TRUE
)

# file.remove(here("figures/tmp/Fig5A1.pdf"))
upset_down <- image_ggplot(image_read_pdf(here("figures/tmp/Fig5A2.pdf")),
  interpolate = TRUE
)
# file.remove(here("figures/tmp/Fig5A2.pdf"))

fig5 <- (upset_up | upset_down) /
  ((pr_plot_mm / pr_plot_category) + plot_layout(heights = c(1, 1))) +
  plot_layout(height = c(1, 3)) +
  plot_annotation(tag_levels = list(c("A", "", "B", "C"))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

fig5

Version Author Date
1f624c7 christianholland 2021-02-27
c2af37f christianholland 2020-12-29

ggsave(here("figures/Figure 5.pdf"), fig5,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 5.png"), fig5,
  width = 21, height = 29.7, units = c("cm")
)

Supplementary Figure 5.1

sfig5_1 <- ((num_genes | gs_sim) + plot_layout(width = c(1, 2))) +
  # ((tile_up + box_up) + plot_layout(width = c(4, 1), guides = "collect")) /
  # ((tile_down + box_down) + plot_layout(width = c(4, 1), guides = "collect")) +
  plot_annotation(tag_levels = list(c("A", "B", "C", "", "D"))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

sfig5_1

Version Author Date
1f624c7 christianholland 2021-02-27
73708b1 christianholland 2020-12-29
c2af37f christianholland 2020-12-29

ggsave(here("figures/Supplementary Figure 5.1.pdf"), sfig5_1,
  width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 5.1.png"), sfig5_1,
  width = 21, height = 10, units = c("cm")
)

Time spend to execute this analysis: 00:53 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] gtools_3.8.2             patchwork_1.1.1          magick_2.5.2            
#>  [4] ggrepel_0.9.0            lemon_0.4.5              UpSetR_1.4.0            
#>  [7] scales_1.1.1             AachenColorPalette_1.1.2 here_1.0.1              
#> [10] tidylog_1.0.2            forcats_0.5.0            stringr_1.4.0           
#> [13] dplyr_1.0.2              purrr_0.3.4              readr_1.4.0             
#> [16] tidyr_1.1.2              tibble_3.0.4             ggplot2_3.3.2           
#> [19] tidyverse_1.3.0          workflowr_1.6.2         
#> 
#> loaded via a namespace (and not attached):
#>  [1] httr_1.4.2        viridisLite_0.3.0 jsonlite_1.7.2    modelr_0.1.8     
#>  [5] pdftools_2.3.1    assertthat_0.2.1  askpass_1.1       renv_0.12.3      
#>  [9] cellranger_1.1.0  yaml_2.2.1        qpdf_1.1          pillar_1.4.7     
#> [13] backports_1.2.1   lattice_0.20-41   glue_1.4.2        digest_0.6.27    
#> [17] promises_1.1.1    rvest_0.3.6       colorspace_2.0-0  cowplot_1.1.0    
#> [21] htmltools_0.5.0   httpuv_1.5.4      plyr_1.8.6        clisymbols_1.2.0 
#> [25] pkgconfig_2.0.3   broom_0.7.3       haven_2.3.1       whisker_0.4      
#> [29] later_1.1.0.1     git2r_0.27.1      generics_0.1.0    farver_2.0.3     
#> [33] ellipsis_0.3.1    withr_2.3.0       cli_2.2.0         magrittr_2.0.1   
#> [37] crayon_1.3.4      readxl_1.3.1      evaluate_0.14     fs_1.5.0         
#> [41] fansi_0.4.1       xml2_1.3.2        tools_4.0.2       hms_0.5.3        
#> [45] lifecycle_0.2.0   munsell_0.5.0     reprex_0.3.0      compiler_4.0.2   
#> [49] rlang_0.4.9       rstudioapi_0.13   labeling_0.4.2    rmarkdown_2.6    
#> [53] gtable_0.3.0      codetools_0.2-16  DBI_1.1.0         R6_2.5.0         
#> [57] gridExtra_2.3     lubridate_1.7.9.2 knitr_1.30        rprojroot_2.0.2  
#> [61] stringi_1.5.3     Rcpp_1.0.5        vctrs_0.3.6       dbplyr_2.0.0     
#> [65] tidyselect_1.1.0  xfun_0.19