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Knit directory: liver-disease-atlas/

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Rmd af84a7a christianholland 2020-12-23 added plotting script chronic ccl4

Introduction

Here we generate publication ready plots of the analysis of the chronic CCl4 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(VennDiagram)
library(grid)
library(gridExtra)
library(patchwork)

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

Definition of global variables that are used throughout this analysis.

# i/o
data_path <- "data/mouse-chronic-ccl4"
output_path <- "output/mouse-chronic-ccl4"

# graphical parameters
# fontsize
fz <- 9

# 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")) +
  theme(plot.margin = margin(r = 1, unit = "cm"))

Histology

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

Liver enyzmes

df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
  mutate(time = parse_number(time),
         time = replace_na(time, 0)) %>%
  mutate(time = ordered(time)) %>%
  pivot_longer(col = -c(time), names_to = "enzyme", values_to = "y") %>%
  mutate(enzyme = factor(str_to_upper(enzyme), levels = c("ALT", "AST", "ALP")))

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

liver_enzymes_partial <- df %>%
  ggplot(aes(x = time, y = y)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.5) +
  # geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
  # geom_col() +
  facet_rep_wrap(~enzyme, scales = "free", ncol = 3) +
  labs(x = "Time in month", y = "U/L") +
  my_theme(grid = "y", fsize = fz) +
  stat_compare_means(
    data = df, label = "p.signif",
    ref.group = "0", hide.ns = T
  )

liver_enzymes <- df %>%
  ggplot(aes(x = time, y = y)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.5) +
  # geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
  # geom_col() +
  facet_rep_wrap(~enzyme, scales = "free", ncol = 1) +
  labs(x = "Time in month", y = "U/L") +
  my_theme(grid = "y", fsize = fz) +
  stat_compare_means(
    data = df, label = "p.signif",
    ref.group = "0", hide.ns = T
  )

liver_enzymes

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PCA

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

keys <- key_mm %>%
  filter(treatment == "CCl4" & class == "Chronic") %>%
  distinct(time = value, label = time_label2) %>%
  drop_na() %>%
  add_row(time = 0, label = "Control") %>%
  mutate(time = ordered(time))

pca_plot <- pca_result$coords %>%
  arrange(time) %>%
  inner_join(keys, by = "time") %>%
  mutate(label = fct_inorder(label)) %>%
  mutate(treatment = case_when(treatment == "oil" ~ "Oil",
                               treatment == "ccl4" ~ "CCl4 + Oil",
                               treatment == "ctrl" ~ "Control")) %>%
  ggplot(aes(x=PC1, y=PC2, color=label, shape = treatment, label = label)) +
  geom_point() +
  labs(x = paste0("PC1", " (", pca_result$var[1], "%)"),
       y = paste0("PC2", " (", pca_result$var[2], "%)"),
       color = "Time", shape = "Treatment") +
  my_theme(fsize  = fz) +
  theme(legend.position = "top",
        legend.box.margin = margin(10, 0, -20, 10)) +
  scale_color_manual(values = aachen_color(c(
    "violet", "bordeaux", "red", "orange"
  )))

pca_plot

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Volcano plot

df <- readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  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))

volcano <- df %>%
  plot_volcano(ncol = 1) +
  geom_text(
    data = filter(deg_count),
    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)

volcano

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Top DEGs

df <- readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  inner_join(key_mm) %>%
  select(-class) %>%
  rename(class = time_label2)

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

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

# for main panel
top_genes <- plots %>%
  pull(p) %>%
  wrap_plots() +
  plot_layout(ncol = 1)

top_genes

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Gene overlap

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

tables = df %>%
  filter(contrast_reference == "pure_ccl4") %>%
  mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
  select(-contrast_reference, -contrast) %>%
  mutate(class = factor(class, 
                        levels = c("Month 2", "Month 6", "Month 12"))) %>%
  group_split(class)

# extract labellers
c1 = tables[[1]] %>% distinct(class) %>% pull() %>% as.character()
c2 = tables[[2]] %>% distinct(class) %>% pull() %>% as.character()
c3 = tables[[3]] %>% distinct(class) %>% pull() %>% as.character()

t1 = tables[[1]] %>% count(regulation)
t2 = tables[[2]] %>% count(regulation)
t3 = tables[[3]] %>% count(regulation)
    
plots = c("up", "down") %>%
  map(function(r) {
    # set sizes of regulated genes
    a1 = t1 %>% filter(regulation == r) %>% pull(n)
    a2 = t2 %>% filter(regulation == r) %>% pull(n)
    a3 = t3 %>% filter(regulation == r) %>% pull(n)
    
    a12 = purrr::reduce(
      list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
           tables[[2]] %>% filter(regulation == r) %>% pull(gene)),
      intersect) %>%
      length()
    
    a23 = purrr::reduce(
      list(tables[[2]] %>% filter(regulation == r) %>% pull(gene),
           tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
      intersect) %>% 
      length()
    
    a13 = purrr::reduce(
      list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
           tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
      intersect) %>% 
      length()
    
    a123 = purrr::reduce(
      list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
           tables[[2]] %>% filter(regulation == r) %>% pull(gene),
           tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
      intersect) %>% 
      length()
    

    
    grid.newpage()
    p = draw.triple.venn(
      area1 = a1, area2 = a2, area3 = a3, 
      n12 = a12, n23 = a23, n13 = a13, 
      n123 = a123,
      category = c(c1, c2, c3),
      # lty = "blank",
      cex = 1/12*fz,
      fontfamily = rep("sans", 7),
      # fill = aachen_color(c("purple", "petrol", "red")),
      # cat.col = aachen_color(c("purple", "petrol", "red")),
      cat.cex = 1/12*(fz+1),
      cat.fontfamily = rep("sans", 3),
      cat.pos = c(350,10,180),
      cat.prompts = T,
      cat.just = list(c(0.5, 1), c(0.5, 1), c(0.5, 1))
    ) %>%
      as_ggplot() %>%
      grid.arrange(top = textGrob(str_to_title(r), gp=gpar(fontsize=fz, 
                                             fontface = "bold")))
  })

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gene_overlap = wrap_plots(plots)

Top gene of overlap

df = readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  inner_join(key_mm) %>%
  select(-class) %>%
  rename(class = time_label2) %>% 
  filter(regulation != "ns") %>%
  mutate(regulation = fct_inorder(str_to_title(regulation)))
  
top_genes_ranked = df %>%
  # filter for genes that are deregulated at all time points
  group_by(gene, regulation) %>%
  filter(n() == 3) %>%
  summarise(mean_logfc = mean(logFC)) %>%
  group_by(regulation) %>%
  mutate(rank = row_number(-abs(mean_logfc))) %>%
  ungroup()

top_genes_of_overlap = df %>%
  inner_join(top_genes_ranked, by=c("gene", "regulation"))

top_overlap_genes = top_genes_of_overlap %>%
  filter(rank <= 5) %>%
  ggplot(aes(x=fct_reorder(gene, mean_logfc), y=logFC, group = class, 
             fill = class)) +
  geom_col(position = "dodge") +
  facet_rep_wrap(~regulation, ncol = 1, scales = "free") +
  my_theme(grid = "y", fsize = fz) +
  labs(x="Gene", y="logFC", fill = NULL) +
  theme(legend.position = "top",
        legend.box.margin = margin(-2, 0, -10, 0)) +
  scale_fill_manual(values = aachen_color(c("maygreen", "green", "turquoise"))) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

top_overlap_genes

Version Author Date
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Time series cluster

profile_label = tribble(
  ~profile, ~process,
  "STEM ID: 14", "Inflammation",
  "STEM ID: 6", "Proliferation",
  "STEM ID: 17", NA_character_,
  "STEM ID: 12", NA_character_,
  "STEM ID: 7", "Metabolism (1)",
  "STEM ID: 9", "Metabolism (2)",
  "STEM ID: 13", "ECM"
) %>%
  mutate(profile = fct_inorder(profile),
         process = coalesce(process, profile),
         process = fct_inorder(process))

stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
  filter(key == "pure_ccl4") %>%
  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)

# 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 = 2) +
  labs(x = "Time in Months") +
  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 = 1)
  )

ts_cluster

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Collage

Figure 1

fig1 <- (design + histology) /
  ((liver_enzymes | (gene_overlap / plot_spacer()) | top_overlap_genes) +
     plot_layout(widths = c(1,2.5,2))) /
  ts_cluster +
  plot_layout(height = c(1, 1, 1)) +
  plot_annotation(tag_levels = list(c("A", "B", "C", "D", "", "E", "F"))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

fig1

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ggsave(here("figures/Figure 1.pdf"), fig1,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 1.png"), fig1,
  width = 21, height = 29.7, units = c("cm")
)

Partial Figure 1

Supplementary Figure 1.1

sfig1_1 <- pca_plot / (volcano | top_genes) +
  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")
  )

sfig1_1

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ggsave(here("figures/Supplementary Figure 1.1.pdf"), sfig1_1,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 1.1.png"), sfig1_1,
  width = 21, height = 29.7, units = c("cm")
)

Time spend to execute this analysis: 00:55 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          gridExtra_2.3            VennDiagram_1.6.20      
#>  [4] futile.logger_1.4.3      ggpubr_0.4.0             lemon_0.4.5             
#>  [7] cowplot_1.1.0            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] fs_1.5.0             lubridate_1.7.9.2    httr_1.4.2          
#>  [4] rprojroot_2.0.2      tools_4.0.2          backports_1.2.1     
#>  [7] R6_2.5.0             DBI_1.1.0            colorspace_2.0-0    
#> [10] withr_2.3.0          tidyselect_1.1.0     curl_4.3            
#> [13] compiler_4.0.2       git2r_0.27.1         cli_2.2.0           
#> [16] rvest_0.3.6          formatR_1.7          xml2_1.3.2          
#> [19] labeling_0.4.2       scales_1.1.1         digest_0.6.27       
#> [22] foreign_0.8-80       rmarkdown_2.6        rio_0.5.16          
#> [25] pkgconfig_2.0.3      htmltools_0.5.0      dbplyr_2.0.0        
#> [28] rlang_0.4.9          readxl_1.3.1         rstudioapi_0.13     
#> [31] farver_2.0.3         generics_0.1.0       jsonlite_1.7.2      
#> [34] gtools_3.8.2         zip_2.1.1            car_3.0-10          
#> [37] magrittr_2.0.1       Rcpp_1.0.5           munsell_0.5.0       
#> [40] fansi_0.4.1          abind_1.4-5          lifecycle_0.2.0     
#> [43] stringi_1.5.3        whisker_0.4          yaml_2.2.1          
#> [46] carData_3.0-4        plyr_1.8.6           promises_1.1.1      
#> [49] crayon_1.3.4         lattice_0.20-41      haven_2.3.1         
#> [52] hms_0.5.3            magick_2.5.2         knitr_1.30          
#> [55] pillar_1.4.7         ggsignif_0.6.0       codetools_0.2-16    
#> [58] clisymbols_1.2.0     futile.options_1.0.1 reprex_0.3.0        
#> [61] glue_1.4.2           evaluate_0.14        lambda.r_1.2.4      
#> [64] data.table_1.13.4    renv_0.12.3          modelr_0.1.8        
#> [67] vctrs_0.3.6          httpuv_1.5.4         cellranger_1.1.0    
#> [70] gtable_0.3.0         assertthat_0.2.1     xfun_0.19           
#> [73] openxlsx_4.2.3       broom_0.7.3          rstatix_0.6.0       
#> [76] later_1.1.0.1        ellipsis_0.3.1