Last updated: 2021-06-13

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Knit directory: liver-disease-atlas/

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Rmd 7f331d0 christianholland 2021-02-28 wflow_publish("analysis/*", delete_cache = TRUE, republish = TRUE)

Introduction

Here we generate publication-ready plots for the comparison of the chronic CCl4 model and patient cohorts.

Libraries and sources

These libraries and sources are used for this analysis.

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

library(AachenColorPalette)
library(VennDiagram)
library(gridExtra)
library(ggpubr)
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-mouse-vs-human"
output_path <- "output/meta-mouse-vs-human"

# 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"))
key_hs <- readRDS(here("data/meta-mouse-vs-human/contrast_annotation.rds"))

Similarity of patient cohort gene sets

keys <- key_hs %>%
  distinct(contrast, label, source, phenotype)

j <- readRDS(here(output_path, "gene_set_similarity.rds")) %>%
  separate(set1, into = c("source", "phenotype", "contrast"), sep = "-") %>%
  inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
  select(-source, -phenotype, -contrast) %>%
  rename(set1 = label) %>%
  separate(set2, into = c("source", "phenotype", "contrast"), sep = "-") %>%
  inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
  rename(set2 = label) %>%
  select(set1, set2, similarity)

patient_gs_sim <- j %>%
  mutate(set1 = fct_rev(set1)) %>%
  ggplot(aes(
    x = set1, y = set2, fill = similarity,
    label = round(similarity, 3)
  )) +
  geom_tile(color = "black") +
  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()
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  geom_text(size = (fz - 2) / (14 / 5)) +
  my_theme(fsize = fz, grid = "no")

patient_gs_sim

Version Author Date
3340593 christianholland 2021-02-28

Interstudy enrichment of patient cohorts

keys <- key_hs %>%
  distinct(contrast, label, source, phenotype)

gsea_res <- readRDS(here(output_path, "interstudy_enrichment.rds")) %>%
  separate(signature,
    into = c("source", "phenotype", "contrast"),
    sep = "-"
  ) %>%
  inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
  select(-source, -phenotype, -contrast) %>%
  rename(signature = label) %>%
  separate(geneset, into = c("source", "phenotype", "contrast"), sep = "-") %>%
  inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
  rename(geneset = label)

patient_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 = 2) +
  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"))

patient_interstudy_enrichment

Version Author Date
3340593 christianholland 2021-02-28

Mouse enrichment in patients

contrast_keys_mouse <- key_mm %>%
  distinct(signature = contrast, label2)
contrast_keys_human <- key_hs %>%
  unite(geneset, source, phenotype, contrast, sep = "-") %>%
  distinct(geneset, label)

gsea_res <- readRDS(here(output_path, "gsea_res.rds")) %>%
  inner_join(contrast_keys_mouse, by = "signature") %>%
  select(-signature) %>%
  rename(signature = label2) %>%
  inner_join(contrast_keys_human, by = "geneset") %>%
  select(-geneset) %>%
  rename(geneset = label) %>%
  mutate(
    label = gtools::stars.pval(padj),
    direction = fct_rev(str_to_title(direction))
  )

mm_enrichment_in_hs <- gsea_res %>%
  ggplot(aes(x = signature, y = geneset, fill = ES, label = label)) +
  geom_tile() +
  facet_wrap(~direction) +
  scale_fill_gradient2(
    low = aachen_color("blue"), mid = "white",
    high = aachen_color("red")
  ) +
  my_theme(grid = "no", fsize = fz) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.line = element_blank(),
    axis.ticks = element_blank()
  ) +
  geom_text(size = fz / (14 / 5), vjust = 1) +
  labs(x = "Signature", y = "Gene Set")

mm_enrichment_in_hs

Version Author Date
3340593 christianholland 2021-02-28

Overlap of leading-edge genes

keys <- key_mm %>%
  distinct(signature = contrast, class = time_label2)

le <- readRDS(here(output_path, "leading_edges.rds")) %>%
  inner_join(keys, by = "signature") %>%
  rename(regulation = direction) %>%
  arrange(class)

tables <- le %>%
  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)

le_overlap_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()
    grid.grabExpr(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(0, 0, 180),
      cat.prompts = T
    )) %>%
      as_ggplot() %>%
      grid.arrange(top = textGrob(str_to_title(r), gp = gpar(
        fontsize = fz,
        fontface = "bold"
      )))
  })

Version Author Date
3340593 christianholland 2021-02-28

Version Author Date
3340593 christianholland 2021-02-28

le_gene_overlap <- wrap_plots(le_overlap_plots)

Top human-mouse consistent genes

Heatmap

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

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

keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)

# load consistent genes
df <- readRDS(here(output_path, "consistent_genes.rds"))

# extract top 100 consistent genes
top_genes_df <- df %>%
  filter(rank <= 100) %>%
  inner_join(keys, by = "contrast") %>%
  mutate(label = fct_drop(label))

# build matrix for heatmap
m <- top_genes_df %>%
  distinct(gene, label, logFC) %>%
  spread(label, logFC) %>%
  data.frame(row.names = 1, check.names = F) %>%
  as.matrix()

# build cell type annotation
celltype_anno <- top_genes_df %>%
  distinct(gene, celltype, adjusted_logfc) %>%
  spread(celltype, adjusted_logfc, fill = 0) %>%
  data.frame(row.names = 1, check.names = F) %>%
  select(-Unknown)

col_fun_anno <- colorRamp2(
  c(-1.5, 0, 1.5),
  c(aachen_color("blue"), "white", aachen_color("red"))
)
ha <- HeatmapAnnotation(
  df = celltype_anno,
  show_legend = F,
  border = F,
  gap = unit(0.25, "mm"),
  col = list(
    MPs = col_fun_anno, pDCs = col_fun_anno, ILCs = col_fun_anno,
    Tcells = col_fun_anno, Bcells = col_fun_anno,
    `Plasma Bcells` = col_fun_anno, `Mast cells` = col_fun_anno,
    Endothelia = col_fun_anno, Mesenchyme = col_fun_anno,
    Mesothelia = col_fun_anno, Hepatocytes = col_fun_anno,
    Cholangiocytes = col_fun_anno
  ),
  annotation_name_gp = gpar(fontsize = fz - 1),
  simple_anno_size = unit(0.15, "cm")
)

# build heatmap
h_hmap <- Heatmap(
  t(as.matrix(m)),
  col = col_fun,
  cluster_rows = T,
  cluster_columns = T,
  show_row_dend = F,
  show_column_dend = 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,
  top_annotation = ha,
  row_split = c(rep("Mouse", 3), rep("Human", 15)),
  row_title_gp = gpar(fontsize = fz + 1),
)

h_hmap

Version Author Date
3340593 christianholland 2021-02-28

Characteriatzion via TFs

ora_res <- readRDS(here(
  output_path,
  "leading_edges_characterization.rds"
)) %>%
  filter(group %in% c("dorothea") & fdr <= 0.2) %>%
  mutate(regulation = str_to_title(regulation))

tfs_up <- ora_res %>%
  filter(regulation == "Up") %>%
  filter(geneset %in% c("SP1", "RELA", "NFKB1")) %>%
  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)

# no significant hits
# 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)

tfs_up

Version Author Date
3340593 christianholland 2021-02-28

Characteriatzion via pathways

ora_res <- readRDS(here(
  output_path,
  "leading_edges_characterization.rds"
)) %>%
  filter(group %in% c("progeny") & fdr <= 0.2) %>%
  mutate(regulation = str_to_title(regulation))

pw_up <- ora_res %>%
  filter(regulation == "Up") %>%
  filter(geneset %in% c("TGFb", "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)

pw_down <- ora_res %>%
  filter(regulation == "Down") %>%
  filter(geneset %in% c("Androgen")) %>%
  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)

pw_up + pw_down

Version Author Date
3340593 christianholland 2021-02-28

Characterization via GO terms

# get wordcounts for go terms
wordcounts <- readRDS(here(output_path, "go_wordcounts.rds"))

# get ranking of go clusters
cluster_ranking <- readRDS(here(output_path, "go_cluster_ranking.rds")) %>%
  group_by(cluster) %>%
  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("green", "purple"))) +
  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)

consistent_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)

consistent_down <- down_dens +
  inset_element(down_cloud,
    left = 0, bottom = 0, right = 1, top = 1,
    align_to = "panel"
  )

consistent_up + consistent_down

Version Author Date
3340593 christianholland 2021-02-28

Expression of consistent genes in mouse

keys <- key_mm %>%
  distinct(contrast, label = time_label2)

le <- readRDS(here(output_path, "leading_edges_mgi.rds")) %>%
  rename(contrast = signature)

c <- readRDS(here("output/mouse-chronic-ccl4/limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  left_join(le) %>%
  select(-regulation) %>%
  rename(regulation = direction) %>%
  replace_na(list(regulation = "ns")) %>%
  mutate(regulation = factor(regulation, levels = c("up", "down", "ns"))) %>%
  mutate(regulation = fct_recode(regulation,
    Up = "up", Down = "down",
    n.s. = "ns"
  )) %>%
  inner_join(keys, by = "contrast") %>%
  arrange(desc(regulation))

consistent_volcano_main <- c %>%
  filter(label == "Month 12") %>%
  ggplot(aes(
    x = logFC, y = -log10(pval), color = regulation,
    alpha = regulation
  )) +
  geom_point() +
  facet_rep_wrap(~label, scales = "free") +
  my_theme(grid = "y", fsize = fz) +
  scale_alpha_manual(values = c(0.7, 0.7, 0.2), guide = "none", drop = F) +
  scale_color_manual(
    values = c(aachen_color(c("red", "blue", "black50"))),
    breaks = c("Up", "Down"), labels = c("Up", "Down"),
    drop = F
  ) +
  labs(
    x = "logFC", y = expression(-log["10"] * "(p-value)"),
    color = str_wrap("Regulation", width = 15)
  )

consistent_volcano_supp <- c %>%
  filter(label != "Month 12") %>%
  ggplot(aes(
    x = logFC, y = -log10(pval), color = regulation,
    alpha = regulation
  )) +
  geom_point() +
  facet_rep_wrap(~label, scales = "free") +
  my_theme(grid = "y", fsize = fz) +
  scale_alpha_manual(values = c(0.7, 0.7, 0.2), guide = "none", drop = F) +
  scale_color_manual(
    values = c(aachen_color(c("red", "blue", "black50"))),
    breaks = c("Up", "Down"), labels = c("Up", "Down"),
    drop = F
  ) +
  labs(
    x = "logFC", y = expression(-log["10"] * "(p-value)"),
    color = str_wrap("Regulation", width = 15)
  )

consistent_volcano_main + consistent_volcano_supp

Version Author Date
3340593 christianholland 2021-02-28

Cross-species correlation of basal gene expression levels

basal_genes <- readRDS(here(output_path, "basel_gene_expression_levels.rds"))

basal_corr_plot <- map(basal_genes, function(data) {
  data %>%
    ggplot(aes(x = human, y = mouse)) +
    geom_point(size = 0.1) +
    geom_abline(color = aachen_color("red")) +
    my_theme() +
    coord_fixed() +
    labs(
      x = expression(-log["2"] * (CPM["Human"])),
      y = expression(-log["2"] * (CPM["Mouse"]))
    )
}) %>%
  wrap_plots()

Collage

Figure 4

Main Figure.


fig4 <- patient_interstudy_enrichment /
  ((mm_enrichment_in_hs | le_overlap_plots[[1]] | le_overlap_plots[[2]]) +
    plot_layout(widths = c(1, 2.25, 1.75))) /
  grid.grabExpr(draw(h_hmap)) /
  ((pw_up | tfs_up | consistent_up | consistent_volcano_main) +
    plot_layout(width = c(0.2, 0.3, 1.5, 1.5))) +
  plot_layout(height = c(0.75, 0.75, 1.75, 0.75)) +
  plot_annotation(tag_levels = list(c(
    "A", "B", "C", "", "D", "E", "F", "G",
    "", "H"
  ))) &
  theme(
    plot.tag = element_text(size = fz + 3, face = "bold"),
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  )

fig4

Version Author Date
3340593 christianholland 2021-02-28

ggsave(here("figures/Figure 4.pdf"), fig4,
  width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 4.png"), fig4,
  width = 21, height = 29.7, units = c("cm")
)

Supplementary Figure 4.1

Gene similarity of patient cohorts and basal correlation plot.

sfig4_1 <- patient_gs_sim / basal_corr_plot &
  theme(
    legend.key.height = unit(11.5, "pt"),
    legend.key.width = unit(12.5, "pt")
  ) &
  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")
  )

sfig4_1

Version Author Date
3340593 christianholland 2021-02-28

ggsave(here("figures/Supplementary Figure 4.1.pdf"), sfig4_1,
  width = 21, height = 15, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 4.1.png"), sfig4_1,
  width = 21, height = 15, units = c("cm")
)

Supplementary Figure 4.2

Characterization of down-regulated consistent genes.

sfig4_2 <- pw_down + consistent_down +
  plot_layout(widths = c(1, 8)) +
  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")
  )

sfig4_2

Version Author Date
3340593 christianholland 2021-02-28

ggsave(here("figures/Supplementary Figure 4.2.pdf"), sfig4_2,
  width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 4.2.png"), sfig4_2,
  width = 21, height = 10, units = c("cm")
)

Time spend to execute this analysis: 00:41 minutes.


sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> 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] ggpubr_0.4.0             gridExtra_2.3            VennDiagram_1.6.20      
#> [10] futile.logger_1.4.3      AachenColorPalette_1.1.2 here_1.0.1              
#> [13] tidylog_1.0.2            forcats_0.5.0            stringr_1.4.0           
#> [16] dplyr_1.0.2              purrr_0.3.4              readr_1.4.0             
#> [19] tidyr_1.1.2              tibble_3.0.4             ggplot2_3.3.2           
#> [22] tidyverse_1.3.0          workflowr_1.6.2         
#> 
#> loaded via a namespace (and not attached):
#>  [1] colorspace_2.0-0     ggsignif_0.6.0       rjson_0.2.20        
#>  [4] ellipsis_0.3.1       rio_0.5.16           rprojroot_2.0.2     
#>  [7] GlobalOptions_0.1.2  fs_1.5.0             clue_0.3-58         
#> [10] rstudioapi_0.13      farver_2.0.3         fansi_0.4.1         
#> [13] lubridate_1.7.9.2    xml2_1.3.2           codetools_0.2-18    
#> [16] knitr_1.30           jsonlite_1.7.2       broom_0.7.3         
#> [19] cluster_2.1.1        dbplyr_2.0.0         png_0.1-7           
#> [22] compiler_4.0.5       httr_1.4.2           backports_1.2.1     
#> [25] assertthat_0.2.1     cli_2.2.0            later_1.1.0.1       
#> [28] formatR_1.7          htmltools_0.5.0      tools_4.0.5         
#> [31] gtable_0.3.0         glue_1.4.2           Rcpp_1.0.5          
#> [34] carData_3.0-4        cellranger_1.1.0     vctrs_0.3.6         
#> [37] xfun_0.19            openxlsx_4.2.3       rvest_0.3.6         
#> [40] lifecycle_0.2.0      renv_0.12.3          gtools_3.8.2        
#> [43] rstatix_0.6.0        clisymbols_1.2.0     hms_0.5.3           
#> [46] promises_1.1.1       parallel_4.0.5       lambda.r_1.2.4      
#> [49] RColorBrewer_1.1-2   yaml_2.2.1           curl_4.3            
#> [52] stringi_1.5.3        zip_2.1.1            shape_1.4.5         
#> [55] rlang_0.4.9          pkgconfig_2.0.3      evaluate_0.14       
#> [58] lattice_0.20-41      labeling_0.4.2       cowplot_1.1.0       
#> [61] tidyselect_1.1.0     plyr_1.8.6           magrittr_2.0.1      
#> [64] R6_2.5.0             generics_0.1.0       DBI_1.1.0           
#> [67] pillar_1.4.7         haven_2.3.1          whisker_0.4         
#> [70] foreign_0.8-81       withr_2.3.0          abind_1.4-5         
#> [73] modelr_0.1.8         crayon_1.3.4         car_3.0-10          
#> [76] futile.options_1.0.1 rmarkdown_2.6        GetoptLong_1.0.5    
#> [79] readxl_1.3.1         data.table_1.13.4    git2r_0.27.1        
#> [82] reprex_0.3.0         digest_0.6.27        httpuv_1.5.4        
#> [85] munsell_0.5.0