Last updated: 2021-06-13

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Knit directory: liver-disease-atlas/

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Introduction

Here we integrate various patient cohorts of chronic liver diseases with the chronic CCl4 mouse model to identify consistently deregulated genes in mouse and human.

Libraries and sources

These libraries and sources are used for this analysis.

library(tidyverse)
library(tidylog)
library(here)
library(readxl)
library(glue)
library(tidytext)
library(janitor)

library(fgsea)
library(dorothea)
library(progeny)
library(biobroom)

library(circlize)
library(AachenColorPalette)
library(lemon)
library(VennDiagram)
library(ComplexHeatmap)
library(gridExtra)
library(cowplot)
library(ggpubr)
library(UpSetR)
library(ggwordcloud)
library(patchwork)

library(msigdf) # remotes::install_github("ToledoEM/msigdf@v7.1")
library(gtools)

options("tidylog.display" = list(print))
source(here("code/utils-utils.R"))
source(here("code/utils-plots.R"))

Definition of global variables 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
# color function for heatmaps
col_fun <- colorRamp2(
  c(-4, 0, 4),
  c(aachen_color("blue"), "white", aachen_color("red"))
)

Merging data of all mouse models

Merging contrasts

Contrasts from all available patient cohorts are merged into a single object.

diehl <- readRDS(here("output/human-diehl-nafld/limma_result.rds")) %>%
  mutate(
    phenotype = "nafld",
    source = "diehl"
  )

hoang <- readRDS(here("output/human-hoang-nafld/limma_result.rds")) %>%
  filter(contrast_reference == "stage_0") %>%
  mutate(
    phenotype = "nafld",
    source = "hoang"
  )
#> filter: no rows removed

hampe13 <- readRDS(here("output/human-hampe13-nash/limma_result.rds")) %>%
  filter(contrast_reference == "control") %>%
  mutate(
    phenotype = "nash",
    source = "hampe13"
  )
#> filter: no rows removed

hampe14 <- readRDS(here("output/human-hampe14-misc/limma_result.rds")) %>%
  filter(contrast_reference == "control") %>%
  mutate(
    phenotype = "omni",
    source = "hampe14"
  )
#> filter: no rows removed

ramnath <- readRDS(here("output/human-ramnath-fibrosis/limma_result.rds")) %>%
  mutate(
    phenotype = "fibrosis",
    source = "ramnath"
  )

combined_contrasts <- bind_rows(
  diehl, ramnath, hoang, hampe13,
  hampe14
) %>%
  select(-contrast_reference) %>%
  mutate(contrast = as_factor(contrast)) %>%
  assign_deg() %>%
  # phenotypes related to obesity are removed
  filter(phenotype != "obesity") %>%
  filter(!str_detect(contrast, "obese"))
#> select: dropped one variable (contrast_reference)
#> filter: no rows removed
#> filter: removed 37,642 rows (13%), 257,688 rows remaining

saveRDS(combined_contrasts, here(output_path, "limma_result.rds"))

Merging meta data

Meta data from all available patient cohorts are merged into a single object.

diehl <- readRDS(here("output/human-diehl-nafld/meta_data.rds")) %>%
  select(sample, group = class) %>%
  mutate(
    phenotype = "nafld",
    source = "diehl"
  )
#> select: renamed one variable (group)

hoang <- readRDS(here("data/human-hoang-nafld/meta_data.rds")) %>%
  select(sample, group = nafld) %>%
  mutate(
    phenotype = "nafld",
    source = "hoang"
  )
#> select: renamed one variable (group) and dropped 6 variables

hampe13 <- readRDS(here("output/human-hampe13-nash/meta_data.rds")) %>%
  select(sample, group) %>%
  mutate(
    phenotype = "nash",
    source = "hampe13"
  )
#> select: dropped 6 variables (gender, inflammation, fibrosis, age, bmi, …)

hampe14 <- readRDS(here("output/human-hampe14-misc/meta_data.rds")) %>%
  select(sample, group) %>%
  mutate(
    phenotype = "omni",
    source = "hampe14"
  )
#> select: dropped 3 variables (gender, age, bmi)

ramnath <- readRDS(here("data/human-ramnath-fibrosis/meta_data.rds")) %>%
  select(sample, group) %>%
  mutate(
    phenotype = "fibrosis",
    source = "ramnath"
  )
#> select: dropped 8 variables (age, gender, disease, stage, infect, …)

combined_meta <- bind_rows(
  diehl, ramnath, hoang, hampe13,
  hampe14
) %>%
  # patients related to obesity are removed
  filter(group != "obese") %>%
  # assign whether patient is sick or healthy (control)
  mutate(class = case_when(
    str_detect(
      group, "early|control|stage_0|mild"
    ) ~ "control",
    TRUE ~ "disease"
  ))
#> filter: removed 51 rows (12%), 372 rows remaining

saveRDS(combined_meta, here(output_path, "meta_data.rds"))

Number of patients

Barplot showing number of patients per study.

df <- readRDS(here(output_path, "meta_data.rds")) %>%
  count(phenotype, source, class)
#> count: now 10 rows and 4 columns, ungrouped

df %>%
  ggplot(aes(
    x = n, fct_reorder(interaction(source, phenotype, sep = "_"), n),
    group = class, fill = class
  )) +
  geom_col(position = "dodge") +
  labs(x = "Number of patients", y = "Study") +
  my_theme(grid = "x", fsize = fz)

Version Author Date
3340593 christianholland 2021-02-28

Gene coverage

Barplot showing the gene coverage of the patient cohorts.

contrasts <- readRDS(here(output_path, "limma_result.rds")) %>%
  distinct(gene, phenotype, source) %>%
  count(phenotype, source)
#> distinct: removed 167,964 rows (65%), 89,724 rows remaining
#> count: now 5 rows and 3 columns, ungrouped

contrasts %>%
  ggplot(aes(
    x = n, fct_reorder(interaction(source, phenotype, sep = "_"), n),
    group = source
  )) +
  geom_col() +
  labs(x = "Gene coverage", y = "Study") +
  my_theme(grid = "x", fsize = fz)

Version Author Date
3340593 christianholland 2021-02-28

PCA of z-scores

To check for batch effects we check the clustering of perturbed samples in PCA space (based on z-scores)

# load z-scores and combine into a single data frame
z_scores_mat <- list.files(here("output"),
  pattern = "z_scores.rds",
  recursive = TRUE, full.names = TRUE
) %>%
  # discard mouse experiments
  keep(str_detect(., pattern = "human-")) %>%
  map(function(path) {
    readRDS(path) %>%
      rownames_to_column("gene")
  }) %>%
  reduce(.f = full_join, by = "gene") %>%
  column_to_rownames("gene")
#> full_join: added 32 columns (GSM1178975, GSM1178980, GSM1178986, GSM1178987, GSM1178988, …)
#>            > rows only in x    3,213
#>            > rows only in y    1,860
#>            > matched rows     16,961
#>            >                 ========
#>            > rows total       22,034
#> full_join: added 72 columns (GSM1501018, GSM1501019, GSM1501022, GSM1501025, GSM1501033, …)
#>            > rows only in x    3,213
#>            > rows only in y        0
#>            > matched rows     18,821
#>            >                 ========
#>            > rows total       22,034
#> full_join: added 74 columns (440349.1.X_1, 440350.1.X_1, 440351.1.X_4, 440352.1.X_4, 440353.1.X_4, …)
#>            > rows only in x    7,183
#>            > rows only in y      342
#>            > matched rows     14,851
#>            >                 ========
#>            > rows total       22,376
#> full_join: added 27 columns (sample14, sample15, sample16, sample18, sample19, …)
#>            > rows only in x    7,392
#>            > rows only in y    1,731
#>            > matched rows     14,984
#>            >                 ========
#>            > rows total       24,107

# remove genes with infinity or NA
z_scores_mat <- z_scores_mat[is.finite(rowSums(z_scores_mat)), ]

# load meta data
meta <- readRDS(here(output_path, "meta_data.rds")) %>%
  filter(class == "disease")
#> filter: removed 135 rows (36%), 237 rows remaining

pca_result <- do_pca(z_scores_mat, meta, top_n_var_genes = NULL)
#> left_join: added 4 columns (group, phenotype, source, class)
#>            > rows only in x     0
#>            > rows only in y  (  0)
#>            > matched rows     237
#>            >                 =====
#>            > rows total       237

saveRDS(pca_result, here(output_path, "z_score_pca.rds"))

plot_pca(pca_result, feature = "phenotype") +
  my_theme()

Version Author Date
a8fc0b0 christianholland 2021-03-29

Number of differential expressed genes

Barplot showing the number of differentially expressed genes for each contrast.

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

combined_contrasts %>%
  filter(regulation != "ns") %>%
  count(contrast, source, regulation) %>%
  ggplot(aes(y = interaction(source, contrast), x = n, fill = regulation)) +
  geom_col(position = position_dodge()) +
  labs(y = NULL, y = "Number of degs") +
  my_theme(grid = "x", fsize = fz)
#> filter: removed 255,692 rows (99%), 1,996 rows remaining
#> count: now 26 rows and 4 columns, ungrouped

Version Author Date
3340593 christianholland 2021-02-28

Interstudy analysis of patient cohorts

Mutual similarity of differential expressed genes

This analysis computes the similarity of differential expressed genes for all contrasts of the patient cohorts. Similarity is measured with the Jaccard Index.

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

# populate gene sets with a fixed size selected by effect size (t-value)
mat_top <- contrasts %>%
  group_by(contrast, phenotype, source) %>%
  top_n(500, abs(statistic)) %>%
  mutate(key = row_number()) %>%
  ungroup() %>%
  unite(geneset, source, phenotype, contrast, sep = "-") %>%
  mutate(geneset = as_factor(geneset)) %>%
  select(geneset, gene, key) %>%
  untdy(key, geneset, gene)
#> top_n (grouped): removed 250,188 rows (97%), 7,500 rows remaining
#> ungroup: no grouping variables
#> select: dropped 5 variables (logFC, statistic, pval, fdr, regulation)
#> select: columns reordered (key, geneset, gene)
#> spread: reorganized (geneset, gene) into (diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, ramnath-fibrosis-nafld_adv_vs_early, hoang-nafld-stage_1_vs_0, hoang-nafld-stage_2_vs_0, …) [was 7500x3, now 500x16]

# usage of jaccard index for balanced set sizes
j <- set_similarity(mat_top, measure = "jaccard", tidy = T)
#> gather: reorganized (diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, ramnath-fibrosis-nafld_adv_vs_early, hoang-nafld-stage_1_vs_0, hoang-nafld-stage_2_vs_0, …) into (set2, similarity) [was 15x16, now 225x3]
#> drop_na: removed 105 rows (47%), 120 rows remaining
#> filter: removed 15 rows (12%), 105 rows remaining
#> mutate_if: converted 'set1' from character to factor (0 new NA)
#>            converted 'set2' from character to factor (0 new NA)

saveRDS(j, here(output_path, "gene_set_similarity.rds"))

j %>%
  ggplot(aes(x = set1, y = set2, fill = similarity)) +
  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)) +
  my_theme(fsize = fz, grid = "no")

Version Author Date
3340593 christianholland 2021-02-28

Mutual enrichment of differential expressed genes

This analysis explores whether the top differential expressed genes of specific contrasts of the acute mouse models are consistently regulated across the acute mouse models.

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

# populate gene sets with a fixed size selected by effect size (t-value)
genesets_top <- contrasts %>%
  mutate(direction = case_when(
    sign(statistic) >= 0 ~ "up",
    sign(statistic) < 0 ~ "down"
  )) %>%
  group_by(source, phenotype, contrast, direction) %>%
  top_n(500, abs(statistic)) %>%
  ungroup() %>%
  unite(geneset, source, phenotype, contrast, sep = "-") %>%
  unite(geneset, geneset, direction, sep = "|") %>%
  mutate(geneset = as_factor(geneset)) %>%
  select(geneset, gene)
#> top_n (grouped): removed 242,688 rows (94%), 15,000 rows remaining
#> ungroup: no grouping variables
#> select: dropped 5 variables (logFC, statistic, pval, fdr, regulation)

# construct signature matrix/data frame
signature_df <- contrasts %>%
  unite(signature, source, phenotype, contrast, sep = "-") %>%
  mutate(signature = as_factor(signature)) %>%
  untdy("gene", "signature", "statistic")
#> select: dropped 4 variables (logFC, pval, fdr, regulation)
#> spread: reorganized (signature, statistic) into (diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, ramnath-fibrosis-nafld_adv_vs_early, hoang-nafld-stage_1_vs_0, hoang-nafld-stage_2_vs_0, …) [was 257688x3, now 24107x16]

# run gsea
set.seed(123)
gsea_res_top <- run_gsea(signature_df, genesets_top, tidy = T) %>%
  separate(geneset, into = c("geneset", "direction"), sep = "[|]") %>%
  mutate(
    signature = as_factor(signature),
    geneset = as_factor(geneset)
  )
#> summarise: now 30 rows and 2 columns, ungrouped
#> rename: renamed one variable (geneset)
#> select: dropped one variable (gene)
#> distinct: removed 14,970 rows (>99%), 30 rows remaining
#> left_join: added no columns
#>            > rows only in x     0
#>            > rows only in y  (  0)
#>            > matched rows     450
#>            >                 =====
#>            > rows total       450

saveRDS(gsea_res_top, here(output_path, "interstudy_enrichment.rds"))

# individual gsea plot
make_gsea_plot(
  signature = signature_df %>%
    rownames_to_column("gene") %>%
    select(gene, t = `hoang-nafld-stage_6_vs_0`) %>%
    drop_na(),
  geneset = genesets_top %>%
    filter(geneset == "diehl-nafld-advanced_vs_mild|up"),
  gene_level_stat = t
)
#> select: renamed one variable (t) and dropped 14 variables
#> drop_na: removed 8,914 rows (37%), 15,193 rows remaining
#> rename: renamed one variable (stat)
#> transmute: changed 15,193 values (100%) of 'stat' (0 new NA)
#>            new variable 'rank' (integer) with 15,193 unique values and 0% NA
#> select: dropped one variable (rank)
#> filter: removed 14,500 rows (97%), 500 rows remaining
#> inner_join: added one column (geneset)
#>             > rows only in x  (14,720)
#>             > rows only in y  (    27)
#>             > matched rows        473
#>             >                 ========
#>             > rows total          473
#> top_n: removed 947 rows (>99%), one row remaining

Version Author Date
3340593 christianholland 2021-02-28

# summary gsea plot
gsea_res_top %>%
  mutate(label = stars.pval(padj)) %>%
  ggplot(aes(x = signature, y = geneset, fill = ES)) +
  geom_tile() +
  geom_text(aes(label = label)) +
  facet_wrap(~direction) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_gradient2() +
  my_theme(fsize = fz, grid = "no") +
  labs(x = "Signature", y = "Gene set")

Version Author Date
3340593 christianholland 2021-02-28

Comparison of mouse and human data

With this analysis we check whether the top differential expressed human genes have the same direction of regulation in the chronic mouse model.

Enrichment of human gene sets in mouse signatures

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

chronic_mouse <- readRDS(
  here("output/mouse-chronic-ccl4/limma_result_hs.rds")
) %>%
  filter(contrast_reference == "pure_ccl4")
#> filter: removed 120,807 rows (75%), 40,269 rows remaining

# populate gene sets with a fixed size selected by effect size (t-value)
genesets_top <- contrasts %>%
  mutate(direction = case_when(
    sign(statistic) >= 0 ~ "up",
    sign(statistic) < 0 ~ "down"
  )) %>%
  group_by(source, phenotype, contrast, direction) %>%
  top_n(500, abs(statistic)) %>%
  ungroup() %>%
  unite(geneset, source, phenotype, contrast, sep = "-") %>%
  unite(geneset, geneset, direction, sep = "|") %>%
  mutate(geneset = as_factor(geneset)) %>%
  select(geneset, gene)
#> top_n (grouped): removed 242,688 rows (94%), 15,000 rows remaining
#> ungroup: no grouping variables
#> select: dropped 5 variables (logFC, statistic, pval, fdr, regulation)

signature_df <- chronic_mouse %>%
  untdy("gene", "contrast", "statistic")
#> select: dropped 5 variables (logFC, pval, fdr, regulation, contrast_reference)
#> spread: reorganized (contrast, statistic) into (pure_ccl_2m_vs_0m, pure_ccl_6m_vs_0m, pure_ccl_12m_vs_0m) [was 40269x3, now 13423x4]

# run gsea
set.seed(123)
gsea_res_top <- run_gsea(signature_df, genesets_top, tidy = T) %>%
  separate(geneset, into = c("geneset", "direction"), sep = "[|]") %>%
  separate(geneset,
    into = c("source", "phenotype", "contrast"), sep = "-",
    remove = F
  ) %>%
  mutate(
    signature = as_factor(signature),
    geneset = as_factor(geneset),
    time = parse_number(as.character(signature))
  )
#> summarise: now 30 rows and 2 columns, ungrouped
#> rename: renamed one variable (geneset)
#> select: dropped one variable (gene)
#> distinct: removed 14,970 rows (>99%), 30 rows remaining
#> left_join: added no columns
#>            > rows only in x    0
#>            > rows only in y  ( 0)
#>            > matched rows     90
#>            >                 ====
#>            > rows total       90

saveRDS(gsea_res_top, here(output_path, "gsea_res.rds"))

# individual gsea plot
make_gsea_plot(
  signature = signature_df %>%
    rownames_to_column("gene") %>%
    select(gene, t = `pure_ccl_12m_vs_0m`) %>%
    drop_na(),
  geneset = genesets_top %>%
    filter(geneset == "diehl-nafld-advanced_vs_mild|up"),
  gene_level_stat = t
)
#> select: renamed one variable (t) and dropped 2 variables
#> drop_na: no rows removed
#> rename: renamed one variable (stat)
#> transmute: changed 13,423 values (100%) of 'stat' (0 new NA)
#>            new variable 'rank' (integer) with 13,423 unique values and 0% NA
#> select: dropped one variable (rank)
#> filter: removed 14,500 rows (97%), 500 rows remaining
#> inner_join: added one column (geneset)
#>             > rows only in x  (12,990)
#>             > rows only in y  (    67)
#>             > matched rows        433
#>             >                 ========
#>             > rows total          433
#> top_n: removed 867 rows (>99%), one row remaining

Version Author Date
3340593 christianholland 2021-02-28

# summary gsea plot
gsea_res_top %>%
  mutate(label = 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"))

Version Author Date
3340593 christianholland 2021-02-28

Leading edge extraction

The enrichment of human genes sets in mouse signatures reveals that there is a set of genes which is significantly consistently deregulated in mouse and human. To identify these genes we extract the leading edge genes from the enrichment analysis.

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

# extract leading edges from significant and correctly directed enrichments
leading_edges <- gsea_res %>%
  filter(padj <= 0.05 &
    (direction == "up" & ES >= 0) | (direction == "down" & ES < 0)) %>%
  unnest(leadingEdge) %>%
  rename(gene = leadingEdge)
#> filter: removed 12 rows (13%), 78 rows remaining
#> rename: renamed one variable (gene)

saveRDS(leading_edges, here(output_path, "individual_le.rds"))

# for each study a union of leading edges is build across all contrast per time
# point
# subsequently we count how often a gene appears per time and direction
# (max 5 times because we have 5 studies in total)
# filter for those leading edges that appear in at least three studies
unified_le <- leading_edges %>%
  distinct(signature, direction, time, source, phenotype, gene) %>%
  count(signature, time, gene, direction, sort = T, name = "n_studies") %>%
  filter(n_studies >= 3)
#> distinct: removed 3,923 rows (32%), 8,316 rows remaining
#> count: now 6,080 rows and 5 columns, ungrouped
#> filter: removed 5,632 rows (93%), 448 rows remaining

# translate to mgi genes for later use
unified_le_mgi <- unified_le %>%
  translate_gene_ids(from = "symbol_hgnc", to = "symbol_mgi") %>%
  distinct(direction, gene, signature) %>%
  # remove predicted genes
  filter(!str_detect(gene, "Gm[0-9]+"))
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> rename: renamed one variable (symbol_hgnc)
#> left_join: added one column (symbol_mgi)
#>            > rows only in x        0
#>            > rows only in y  (30,218)
#>            > matched rows        473    (includes duplicates)
#>            >                 ========
#>            > rows total          473
#> select: renamed one variable (gene) and dropped one variable
#> distinct: removed 6 rows (1%), 467 rows remaining
#> filter: removed 4 rows (1%), 463 rows remaining

# overlap of unified and consistent leading edge genes per time point
v <- unified_le %>%
  rename(regulation = direction) %>%
  mutate(class = str_c("Month ", time)) %>%
  group_split(class) %>%
  plot_venn_diagram()
#> rename: renamed one variable (regulation)
#> distinct: removed 157 rows (99%), one row remaining
#> distinct: removed 124 rows (99%), one row remaining
#> distinct: removed 164 rows (99%), one row remaining
#> count: now 2 rows and 2 columns, ungrouped
#> count: now 2 rows and 2 columns, ungrouped
#> count: now 2 rows and 2 columns, ungrouped
#> filter: removed one row (50%), one row remaining
#> filter: removed one row (50%), one row remaining
#> filter: removed one row (50%), one row remaining
#> filter: removed 73 rows (46%), 85 rows remaining
#> filter: removed 48 rows (38%), 77 rows remaining
#> filter: removed 48 rows (38%), 77 rows remaining
#> filter: removed 68 rows (41%), 97 rows remaining
#> filter: removed 73 rows (46%), 85 rows remaining
#> filter: removed 68 rows (41%), 97 rows remaining
#> filter: removed 73 rows (46%), 85 rows remaining
#> filter: removed 48 rows (38%), 77 rows remaining
#> filter: removed 68 rows (41%), 97 rows remaining

Version Author Date
3340593 christianholland 2021-02-28
#> filter: removed one row (50%), one row remaining
#> filter: removed one row (50%), one row remaining
#> filter: removed one row (50%), one row remaining
#> filter: removed 85 rows (54%), 73 rows remaining
#> filter: removed 77 rows (62%), 48 rows remaining
#> filter: removed 77 rows (62%), 48 rows remaining
#> filter: removed 97 rows (59%), 68 rows remaining
#> filter: removed 85 rows (54%), 73 rows remaining
#> filter: removed 97 rows (59%), 68 rows remaining
#> filter: removed 85 rows (54%), 73 rows remaining
#> filter: removed 77 rows (62%), 48 rows remaining
#> filter: removed 97 rows (59%), 68 rows remaining

Version Author Date
3340593 christianholland 2021-02-28

saveRDS(unified_le, here(output_path, "leading_edges.rds"))
saveRDS(unified_le_mgi, here(output_path, "leading_edges_mgi.rds"))

Heatmap of leading edge genes

# load leading edge genes and human and mouse contrasts
le <- readRDS(here(output_path, "leading_edges.rds"))
contrasts <- readRDS(here(output_path, "limma_result.rds"))
chronic_mouse <- readRDS(
  here("output/mouse-chronic-ccl4/limma_result_hs.rds")
) %>%
  filter(contrast_reference == "pure_ccl4")
#> filter: removed 120,807 rows (75%), 40,269 rows remaining


# filter mouse and human genes for leading edge genes
c <- chronic_mouse %>%
  inner_join(le, by = "gene") %>%
  mutate(class = "chronic") %>%
  select(gene, contrast, logFC, class)
#> inner_join: added 4 columns (signature, time, direction, n_studies)
#>             > rows only in x  (39,585)
#>             > rows only in y  (     0)
#>             > matched rows      1,344    (includes duplicates)
#>             >                 ========
#>             > rows total        1,344
#> select: dropped 9 variables (statistic, pval, fdr, regulation, contrast_reference, …)

h <- contrasts %>%
  inner_join(le, by = "gene") %>%
  mutate(class = "human") %>%
  unite(contrast, source, phenotype, contrast, sep = "-") %>%
  select(gene, contrast, logFC, class)
#> inner_join: added 4 columns (signature, time, direction, n_studies)
#>             > rows only in x  (254,285)
#>             > rows only in y  (      0)
#>             > matched rows       6,681    (includes duplicates)
#>             >                 =========
#>             > rows total         6,681
#> select: dropped 8 variables (statistic, pval, fdr, regulation, signature, …)

df <- bind_rows(c, h) %>%
  mutate(contrast = as_factor(contrast)) %>%
  distinct()
#> distinct: removed 3,938 rows (49%), 4,087 rows remaining

# assign a rank for each gene based on absolute mean logfc
df_ranked <- df %>%
  group_by(gene) %>%
  summarise(mean_logfc = mean(logFC)) %>%
  transmute(gene, rank = row_number(-abs(mean_logfc))) %>%
  inner_join(df, by = "gene")
#> summarise: now 228 rows and 2 columns, ungrouped
#> transmute: dropped one variable (mean_logfc)
#>            new variable 'rank' (integer) with 228 unique values and 0% NA
#> inner_join: added 3 columns (contrast, logFC, class)
#>             > rows only in x  (    0)
#>             > rows only in y  (    0)
#>             > matched rows     4,087    (includes duplicates)
#>             >                 =======
#>             > rows total       4,087

# get degs from single cell data
# see "code/meta-mouse-vs-human/sc_deg.R" to see how this object was created.
sc_degs <- readRDS(here(data_path, "single_cell_degs.rds")) %>%
  enframe("celltype") %>%
  unnest(value) %>%
  filter(p_val_adj <= 0.05) %>%
  mutate(adjusted_logfc = case_when(
    avg_logFC >= 0 & cluster == "Uninjured" ~ -avg_logFC,
    avg_logFC < 0 & cluster == "Uninjured" ~ -avg_logFC,
    TRUE ~ avg_logFC
  )) %>%
  filter(abs(avg_logFC) >= 0.25)
#> filter: removed 19,662 rows (38%), 31,422 rows remaining
#> filter: removed 26,682 rows (85%), 4,740 rows remaining

# combine consistent genes with cell types
df_ranked_ct <- df_ranked %>%
  left_join(sc_degs, by = "gene") %>%
  mutate(celltype = fct_explicit_na(celltype, na_level = "Unknown"))
#> left_join: added 8 columns (celltype, p_val, avg_logFC, pct.1, pct.2, …)
#>            > rows only in x   3,187
#>            > rows only in y  (4,616)
#>            > matched rows     2,232    (includes duplicates)
#>            >                 =======
#>            > rows total       5,419

saveRDS(df_ranked_ct, here(output_path, "consistent_genes.rds"))

mat <- df_ranked_ct %>%
  filter(rank <= 100) %>%
  distinct(gene, contrast, logFC) %>%
  untdy(feature = "gene", key = "contrast", value = "logFC") %>%
  as.matrix()
#> filter: removed 2,675 rows (49%), 2,744 rows remaining
#> distinct: removed 954 rows (35%), 1,790 rows remaining
#> select: no changes
#> spread: reorganized (contrast, logFC) into (pure_ccl_2m_vs_0m, pure_ccl_6m_vs_0m, pure_ccl_12m_vs_0m, diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, …) [was 1790x3, now 100x19]

ComplexHeatmap::Heatmap(t(as.matrix(mat)),
  col = col_fun,
  cluster_rows = F,
  cluster_columns = T,
  row_names_gp = gpar(fontsize = fz), column_names_gp = gpar(fontsize = fz - 4),
  name = "logFC",
  row_gap = unit(2.5, "mm"),
  border = T,
  row_split = c(rep("Mouse", 3), rep("Human", 15))
)

Version Author Date
3340593 christianholland 2021-02-28

Characterization of leading edge genes

Up and down-regulated leading edge genes are characterized with GO terms, PROGENy’s pathways and DoRothEA’s TFs. As statistic over-representation analysis is used.

signatures <- readRDS(here(output_path, "leading_edges.rds")) %>%
  distinct(gene, regulation = direction)
#> distinct: removed 220 rows (49%), 228 rows remaining

genesets <- load_genesets(organism = "human") %>%
  filter(confidence %in% c(NA, "A", "B", "C"))
#> filter: removed 2,643,412 rows (80%), 662,851 rows remaining
#> select: renamed one variable (gene) and dropped 2 variables
#> gather: reorganized (Androgen, EGFR, Estrogen, Hypoxia, JAK-STAT, …) into (geneset, weight) [was 1301x15, now 18214x3]
#> filter: removed 16,814 rows (92%), 1,400 rows remaining
#> select: dropped one variable (weight)
#> select: renamed 2 variables (geneset, gene) and dropped one variable
#> filter: removed 472,718 rows (41%), 678,284 rows remaining

ora_res <- signatures %>%
  nest(sig = -c(regulation)) %>%
  dplyr::mutate(ora = sig %>% map(run_ora,
    sets = genesets, min_size = 10,
    options = list(alternative = "greater"),
    background_n = 20000
  )) %>%
  select(-sig) %>%
  unnest(ora)
#> add_count: new variable 'n' (integer) with 671 unique values and 0% NA
#> filter: removed 13,277 rows (2%), 665,007 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 671 unique values and 0% NA
#> filter: removed 13,277 rows (2%), 665,007 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> select: dropped one variable (sig)

saveRDS(ora_res, here(output_path, "leading_edges_characterization.rds"))

GO downstream analysis

In this section significant GO terms are summarized in two different ways. i) Which words appear the most among the GO terms and ii) Position of members of manually created GO cluster in a ranked list of significant GO terms (based on p-value)

Text analysis

GO terms are splitted into words and their frequency is counted.

# list of words that will be ignored
stop_go_words <- readRDS(here("data/annotation/stop_go_words.rds"))

go_terms <- readRDS(here(output_path, "leading_edges_characterization.rds")) %>%
  filter(group == "go" & fdr <= 0.05) %>%
  mutate(
    term = str_remove(geneset, "GO_"),
    term = str_replace_all(term, "_", " "),
    term = str_to_lower(term)
  ) %>%
  select(regulation, term)
#> filter: removed 4,026 rows (90%), 441 rows remaining
#> select: dropped 8 variables (geneset, group, confidence, contingency_table, estimate, …)

go_wordcounts <- go_terms %>%
  # glue words that should be treated as one
  mutate(
    term = str_replace(term, "smooth muscle", "smoothmuscle"),
    term = str_replace(term, "amino acid", "aminoacid"),
    term = str_replace(term, "cell cycle", "cellcycle"),
    term = str_replace(term, "endoplasmic reticulum stress", "endoplasmicreticulumstress"),
    term = str_replace(term, "endoplasmic reticulum", "endoplasmicreticulum")
  ) %>%
  unnest_tokens(word, term) %>%
  anti_join(stop_words, by = "word") %>%
  # correct for abbreviations
  mutate(word = case_when(
    word == "er" ~ "endoplasmicreticulum",
    TRUE ~ word
  )) %>%
  # remove words which are pure numbers
  filter(!str_detect(word, "^[0-9]+")) %>%
  # count word frequency
  count(regulation, word, sort = T) %>%
  # dissect prior glued words
  mutate(word = case_when(
    word == "smoothmuscle" ~ "smooth-muscle",
    word == "aminoacid" ~ "amino-acid",
    word == "cellcycle" ~ "cell-cycle",
    word == "endoplasmicreticulumstress" ~ "endoplasmic-reticulum-stress",
    word == "endoplasmicreticulum" ~ "endoplasmic-reticulum",
    TRUE ~ word
  )) %>%
  # remove meaning less words
  anti_join(stop_go_words, by = "word")
#> anti_join: added no columns
#>            > rows only in x   1,527
#>            > rows only in y  (1,101)
#>            > matched rows    (  283)
#>            >                 =======
#>            > rows total       1,527
#> filter: removed 2 rows (<1%), 1,525 rows remaining
#> count: now 353 rows and 3 columns, ungrouped
#> anti_join: added no columns
#>            > rows only in x   309
#>            > rows only in y  (  8)
#>            > matched rows    ( 44)
#>            >                 =====
#>            > rows total       309

saveRDS(go_wordcounts, here(output_path, "go_wordcounts.rds"))

go_wordcounts %>%
  filter((regulation == "up" & n > 4) | (regulation == "down" & n > 2)) %>%
  ggplot(aes(label = word, size = n)) +
  geom_text_wordcloud() +
  scale_size_area(max_size = fz / (14 / 5)) +
  facet_wrap(~regulation) +
  theme(axis.line = element_blank()) +
  my_theme(grid = "no", fsize = fz)
#> filter: removed 268 rows (87%), 41 rows remaining

Version Author Date
3340593 christianholland 2021-02-28

Cluster ranking

Distribution of manually created GO-cluster.

go_cluster_mapping <- tribble(
  ~cluster, ~description,
  1, "Migration",
  2, "Development and Morphogenesis",
  3, "Metabolism"
)

go_cluster <- read_excel(here(data_path, "manual_go_cluster_anno.xlsx")) %>%
  distinct(regulation, term, cluster) %>%
  drop_na() %>%
  inner_join(go_cluster_mapping)
#> distinct: no rows removed
#> drop_na: removed 210 rows (52%), 192 rows remaining
#> inner_join: added one column (description)
#>             > rows only in x  (  0)
#>             > rows only in y  (  0)
#>             > matched rows     192
#>             >                 =====
#>             > rows total       192

go_terms_ranking <- readRDS(
  here(output_path, "leading_edges_characterization.rds")
) %>%
  mutate(
    term = str_remove(geneset, "GO_"),
    term = str_replace_all(term, "_", " "),
    term = str_to_lower(term)
  ) %>%
  filter(fdr <= 0.05) %>%
  group_by(regulation) %>%
  mutate(rank = row_number(p.value)) %>%
  mutate(max_rank = max(rank)) %>%
  ungroup() %>%
  select(regulation, term, rank, max_rank)
#> filter: removed 3,997 rows (89%), 470 rows remaining
#> ungroup: no grouping variables
#> select: dropped 8 variables (geneset, group, confidence, contingency_table, estimate, …)

go_cluster_ranking <- go_terms_ranking %>%
  inner_join(go_cluster)
#> inner_join: added 2 columns (cluster, description)
#>             > rows only in x  (292)
#>             > rows only in y  ( 14)
#>             > matched rows     178
#>             >                 =====
#>             > rows total       178

saveRDS(go_cluster_ranking, here(output_path, "go_cluster_ranking.rds"))

go_cluster_ranking %>%
  mutate(regulation = as_factor(regulation)) %>%
  plot_go_rank_density() +
  my_theme(grid = "no", fsize = fz)

Version Author Date
3340593 christianholland 2021-02-28

Integration of published chronic mouse models

In this section the newly generated and previously published chronic mouse models are analyzed in terms of how well they reflect different human etiologies. The differential expressed genes of the already published mouse models is accessed from Teufel et al., 2016, Table S2.

Load Teufel genes

Here the differentially expressed genes of the mouse models from the Teufel study are loaded.

teufel_genes <- read_excel(here(data_path, "TableS2_Teufel_clean.xlsx"),
  sheet = "mouse"
) %>%
  rename(gene = ...1) %>%
  pivot_longer(-gene) %>%
  separate(name, into = c("study", "key")) %>%
  pivot_wider(names_from = key, values_from = value) %>%
  assign_deg(fdr_cutoff = 0.05, effect_size_cutoff = log2(1.5), effect_size_id = logfc) %>%
  mutate(study = str_to_upper(study)) %>%
  mutate(study = factor(study, levels = c(
    "HF12", "HF18", "STZ12", "STZ18", "MCD4",
    "MCD8", "PTEN", "HF30", "WTD"
  )))
#> rename: renamed one variable (gene)
#> pivot_longer: reorganized (hf12_logfc, hf12_fdr, hf18_logfc, hf18_fdr, stz12_logfc, …) into (name, value) [was 1513x19, now 27234x3]
#> pivot_wider: reorganized (key, value) into (logfc, fdr) [was 27234x4, now 13617x4]

# for late use mgi symbols are translated to hgnc
teufel_genes_hs <- teufel_genes %>%
  translate_gene_ids(from = "symbol_mgi", to = "symbol_hgnc") %>%
  drop_na(gene) %>%
  distinct() %>%
  # in case of duplicated genes take the one with the higher logfc
  group_by(study, gene) %>%
  slice_max(n = 1, order_by = abs(logfc)) %>%
  ungroup()
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> rename: renamed one variable (symbol_mgi)
#> left_join: added one column (symbol_hgnc)
#>            > rows only in x    1,539
#>            > rows only in y  (29,020)
#>            > matched rows     12,969    (includes duplicates)
#>            >                 ========
#>            > rows total       14,508
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 1,539 rows (11%), 12,969 rows remaining
#> distinct: removed 171 rows (1%), 12,798 rows remaining
#> slice_max (grouped): removed 222 rows (2%), 12,576 rows remaining
#> ungroup: no grouping variables

saveRDS(teufel_genes, here(output_path, "teufel_genes.rds"))
saveRDS(teufel_genes_hs, here(output_path, "teufel_genes_hs.rds"))

Chronic mouse models in numbers

Number of differentially expressed genes per chronic mouse model.

teufel_genes <- readRDS(here(output_path, "teufel_genes.rds")) %>%
  assign_deg(effect_size_cutoff = log2(1.5), effect_size_id = logfc) %>%
  filter(regulation != "ns")
#> filter: removed 11,046 rows (81%), 2,571 rows remaining
chronic <- readRDS(here("output/mouse-chronic-ccl4/limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  assign_deg(effect_size_cutoff = log2(1.5)) %>%
  filter(regulation != "ns")
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> filter: removed 40,017 rows (87%), 5,997 rows remaining

teufel_count <- teufel_genes %>%
  count(study, regulation)
#> count: now 18 rows and 3 columns, ungrouped

chronic_count <- chronic %>%
  count(contrast, regulation) %>%
  rename(study = contrast)
#> count: now 6 rows and 3 columns, ungrouped
#> rename: renamed one variable (study)

df <- bind_rows(teufel_count, chronic_count)

saveRDS(df, here(output_path, "chronic_mouse_deg_numbers.rds"))

df %>%
  ggplot(aes(y = study, x = n, fill = regulation)) +
  geom_col(position = "dodge") +
  labs(x = "Number of differential expressed genes", y = NULL) +
  scale_x_log10(
    breaks = scales::trans_breaks("log10", function(x) 10^x),
    labels = scales::trans_format("log10", scales::math_format(10^.x))
  ) +
  my_theme(grid = "x", fsize = fz) +
  annotation_logticks(sides = "b")

Version Author Date
3340593 christianholland 2021-02-28

Similarity of chronic mouse models and patient cohorts

patient <- readRDS(here(output_path, "limma_result.rds")) %>%
  select(gene, contrast, logFC, fdr, regulation, source, phenotype, contrast)
#> select: dropped 2 variables (statistic, pval)

teufel <- readRDS(here(output_path, "teufel_genes_hs.rds")) %>%
  rename(contrast = study, logFC = logfc)
#> rename: renamed 2 variables (contrast, logFC)

chronic <- readRDS(here("output/mouse-chronic-ccl4/limma_result_hs.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  select(-c(statistic, pval, contrast_reference))
#> filter: removed 120,807 rows (75%), 40,269 rows remaining
#> select: dropped 3 variables (statistic, pval, contrast_reference)

contrasts <- bind_rows(patient, teufel, chronic) %>%
  assign_deg(effect_size_cutoff = log2(1.5))

# populate gene sets with only significantly expressed genes
mat_sig <- contrasts %>%
  filter(regulation != "ns") %>%
  unite(geneset, source, phenotype, contrast, sep = "-", na.rm = T) %>%
  add_count(geneset, name = "size") %>%
  # exclude gene sets with a size < 20
  filter(size >= 20) %>%
  group_by(geneset) %>%
  mutate(key = row_number()) %>%
  ungroup() %>%
  mutate(geneset = as_factor(geneset)) %>%
  select(geneset, gene, key) %>%
  untdy(key, geneset, gene)
#> filter: removed 296,977 rows (96%), 13,556 rows remaining
#> add_count: new variable 'size' (integer) with 26 unique values and 0% NA
#> filter: removed one row (<1%), 13,555 rows remaining
#> ungroup: no grouping variables
#> select: dropped 4 variables (logFC, fdr, regulation, size)
#> select: columns reordered (key, geneset, gene)
#> spread: reorganized (geneset, gene) into (diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, hoang-nafld-stage_2_vs_0, hoang-nafld-stage_3_vs_0, hoang-nafld-stage_4_vs_0, …) [was 13555x3, now 3640x26]

o <- set_similarity(mat_sig, measure = "overlap_coef", tidy = T)
#> gather: reorganized (diehl-nafld-advanced_vs_mild, ramnath-fibrosis-hcv_adv_vs_early, hoang-nafld-stage_2_vs_0, hoang-nafld-stage_3_vs_0, hoang-nafld-stage_4_vs_0, …) into (set2, similarity) [was 25x26, now 625x3]
#> drop_na: removed 300 rows (48%), 325 rows remaining
#> filter: removed 25 rows (8%), 300 rows remaining
#> mutate_if: converted 'set1' from character to factor (0 new NA)
#>            converted 'set2' from character to factor (0 new NA)

saveRDS(o, here(output_path, "cross_species_similarity.rds"))

o %>%
  ggplot(aes(x = set1, y = set2, fill = similarity)) +
  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)) +
  my_theme(grid = "no", fsize = fz)

Version Author Date
3340593 christianholland 2021-02-28

Enrichment of chronic mouse genes in patient cohorts

patient <- readRDS(here(output_path, "limma_result.rds")) %>%
  select(gene, contrast, logFC, fdr, regulation, source)
#> select: dropped 3 variables (statistic, pval, phenotype)

teufel <- readRDS(here(output_path, "teufel_genes_hs.rds")) %>%
  rename(contrast = study, logFC = logfc)
#> rename: renamed 2 variables (contrast, logFC)

chronic <- readRDS(here("output/mouse-chronic-ccl4/limma_result_hs.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  select(-c(statistic, pval, contrast_reference))
#> filter: removed 120,807 rows (75%), 40,269 rows remaining
#> select: dropped 3 variables (statistic, pval, contrast_reference)

# populate gene sets with only significantly expressed genes
genesets_sig <- bind_rows(teufel, chronic) %>%
  # adapt cutoff for the definition of significant genes
  assign_deg(effect_size_cutoff = log2(1.5)) %>%
  filter(regulation != "ns") %>%
  add_count(contrast, name = "size") %>%
  # exclude gene sets with a size < 10
  filter(size >= 10) %>%
  unite(geneset, contrast) %>%
  unite(geneset, geneset, regulation, sep = "|") %>%
  mutate(geneset = as_factor(geneset)) %>%
  select(geneset, gene)
#> filter: removed 45,192 rows (86%), 7,653 rows remaining
#> add_count: new variable 'size' (integer) with 12 unique values and 0% NA
#> filter: no rows removed
#> select: dropped 3 variables (logFC, fdr, size)

# construct signature matrix/data frame
signature_df <- patient %>%
  unite(signature, source, contrast) %>%
  mutate(signature = as_factor(signature)) %>%
  untdy("gene", "signature", "logFC")
#> select: dropped 2 variables (fdr, regulation)
#> spread: reorganized (signature, logFC) into (diehl_advanced_vs_mild, ramnath_hcv_adv_vs_early, ramnath_nafld_adv_vs_early, hoang_stage_1_vs_0, hoang_stage_2_vs_0, …) [was 257688x3, now 24107x16]

# run gsea
set.seed(123)
gsea_res_sig <- run_gsea(signature_df, genesets_sig, tidy = T, nperm = 10000) %>%
  separate(geneset, into = c("geneset", "direction"), sep = "[|]") %>%
  mutate(
    signature = as_factor(signature),
    geneset = as_factor(geneset)
  )
#> summarise: now 24 rows and 2 columns, ungrouped
#> rename: renamed one variable (geneset)
#> select: dropped one variable (gene)
#> distinct: removed 7,629 rows (>99%), 24 rows remaining
#> left_join: added no columns
#>            > rows only in x     0
#>            > rows only in y  (  0)
#>            > matched rows     360
#>            >                 =====
#>            > rows total       360

saveRDS(gsea_res_sig, here(output_path, "cross_species_enrichment.rds"))

a <- gsea_res_sig %>%
  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) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_gradient2() +
  my_theme(grid = "no", fsize = fz)
#> filter: removed 180 rows (50%), 180 rows remaining

b <- gsea_res_sig %>%
  mutate(direction = fct_rev(direction)) %>%
  filter(direction == "up") %>%
  ggplot(aes(y = geneset, x = ES)) +
  geom_boxplot() +
  # geom_jitter(aes(color = signature)) +
  facet_rep_wrap(~direction, ncol = 1) +
  geom_vline(xintercept = 0) +
  my_theme(fsize = fz, grid = "x")
#> filter: removed 180 rows (50%), 180 rows remaining

a + theme(legend.position = "none") +
  b + theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.line.y = element_blank(),
    axis.ticks.y = element_blank()
  )

Version Author Date
3340593 christianholland 2021-02-28

Build gene pool for human etiology

For each human etiology (e.g. NAFLD) a union of differentially expressed genes in constructed.

# get etiology for each human contrast
etiologies <- readRDS(here(data_path, "contrast_annotation.rds")) %>%
  mutate(etiology = str_remove(disease, pattern = " Stage [0-9]")) %>%
  distinct(source, phenotype, contrast, etiology)
#> distinct: no rows removed

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

etiology_union <- contrasts %>%
  inner_join(etiologies, by = c("source", "phenotype", "contrast")) %>%
  assign_deg(effect_size_cutoff = log2(1.5)) %>%
  filter(regulation != "ns") %>%
  arrange(etiology, gene, regulation) %>%
  # how often is a gene-regulation pair reported per etiology
  count(gene, regulation, etiology) %>%
  group_by(gene, etiology) %>%
  # extract this gene-regulation-etiology combination that is reported by more
  # contrasts
  # in case of ties take the first one (mostly up-regulation, first factor)
  slice_max(n, with_ties = FALSE) %>%
  ungroup() %>%
  select(-n)
#> inner_join: added one column (etiology)
#>             > rows only in x  (      0)
#>             > rows only in y  (      0)
#>             > matched rows     257,688
#>             >                 =========
#>             > rows total       257,688
#> filter: removed 251,785 rows (98%), 5,903 rows remaining
#> count: now 4,627 rows and 4 columns, ungrouped
#> slice_max (grouped): removed 4 rows (<1%), 4,623 rows remaining
#> ungroup: no grouping variables
#> select: dropped one variable (n)

saveRDS(etiology_union, here(output_path, "etiology_gene_sets.rds"))

Etiology genes in numbers

UpsetR plots showing the number of differential expressed genes per etiology and their overlap.

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)
#> filter: removed 1,411 rows (31%), 3,212 rows remaining
#> select: dropped one variable (regulation)
#> spread: reorganized (etiology, val) into (HCV, NAFLD, NASH, PBC, PSC) [was 3212x3, now 2674x6]

grid.newpage()
upset(mat_up,
  nintersects = NA, mainbar.y.label = "Common genes",
  sets.x.label = "Total number of genes"
)
grid.text("Up", x = 0.65, y = 0.95, gp = gpar(fontsize = fz))

Version Author Date
3340593 christianholland 2021-02-28

mat_down <- df %>%
  filter(regulation == "down") %>%
  select(-regulation) %>%
  mutate(val = 1) %>%
  spread(etiology, val, fill = 0) %>%
  data.frame(row.names = 1)
#> filter: removed 3,212 rows (69%), 1,411 rows remaining
#> select: dropped one variable (regulation)
#> spread: reorganized (etiology, val) into (HCV, NAFLD, NASH, PBC, PSC) [was 1411x3, now 1229x6]

grid.newpage()
upset(mat_down,
  nintersects = NA, mainbar.y.label = "Common genes",
  sets.x.label = "Total number of genes"
)
grid.text("Down", x = 0.65, y = 0.95, gp = gpar(fontsize = fz))

Version Author Date
3340593 christianholland 2021-02-28


# overlap in percentages
tabyl(c(rowSums(mat_up), rowSums(mat_down)))
#>  c(rowSums(mat_up), rowSums(mat_down))    n     percent
#>                                      1 3314 0.849090443
#>                                      2  476 0.121957469
#>                                      3   95 0.024340251
#>                                      4   18 0.004611837

Precision and Recall of mouse models

How well mouse models capture the genes that are deregulated in human is computed by precision and recall.

etiology_pool <- readRDS(here(output_path, "etiology_gene_sets.rds")) %>%
  filter(regulation != "ns") %>%
  mutate(key = "key") %>%
  nest(hs_genes = c(gene))
#> filter: no rows removed

teufel_genes <- readRDS(here(output_path, "teufel_genes_hs.rds")) %>%
  filter(regulation != "ns") %>%
  mutate(class = "teufel") %>%
  distinct(gene, study, regulation, class)
#> filter: removed 10,223 rows (81%), 2,353 rows remaining
#> distinct: no rows removed

chronic <- readRDS(here("output/mouse-chronic-ccl4/limma_result_hs.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  select(gene, contrast,
    logfc = logFC, fdr, regulation,
    source = contrast_reference
  ) %>%
  assign_deg(
    fdr_cutoff = 0.05, effect_size_cutoff = log2(1.5),
    effect_size_id = logfc
  ) %>%
  filter(regulation != "ns") %>%
  mutate(class = "chronic") %>%
  distinct(gene, study = contrast, regulation, class)
#> filter: removed 120,807 rows (75%), 40,269 rows remaining
#> select: renamed 2 variables (logfc, source) and dropped 2 variables
#> filter: removed 34,969 rows (87%), 5,300 rows remaining
#> distinct: no rows removed


# merge gene pools of all chronic mouse models
mm <- bind_rows(teufel_genes, chronic)

setup <- mm %>%
  mutate(key = "key") %>%
  # rename(direction = regulation) %>%
  nest(mm_genes = c(gene)) %>%
  inner_join(etiology_pool, by = c("key", "regulation")) %>%
  select(-key)
#> inner_join: added 2 columns (etiology, hs_genes)
#>             > rows only in x  (  0)
#>             > rows only in y  (  0)
#>             > matched rows     120    (includes duplicates)
#>             >                 =====
#>             > rows total       120
#> select: dropped one variable (key)

pr <- setup %>%
  mutate(metrics = pmap(., .f = function(mm_genes, hs_genes, ...) {
    z <- inner_join(mm_genes, hs_genes, by = "gene")

    tibble(
      recall = nrow(z) / nrow(hs_genes),
      recall_ratio = glue("{nrow(z)}/{nrow(hs_genes)}"),
      precision = nrow(z) / nrow(mm_genes),
      precision_ratio = glue("{nrow(z)}/{nrow(mm_genes)}")
    )
  })) %>%
  unnest(metrics)
#> inner_join: added no columns
#>             > rows only in x  ( 65)
#>             > rows only in y  (682)
#>             > matched rows      10
#>             >                 =====
#>             > rows total        10
#> inner_join: added no columns
#>             > rows only in x  ( 74)
#>             > rows only in y  (202)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> inner_join: added no columns
#>             > rows only in x  ( 75)
#>             > rows only in y  (114)
#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  (73)
#>             > rows only in y  (49)
#>             > matched rows      2
#>             >                 ====
#>             > rows total        2
#> inner_join: added no columns
#>             > rows only in x  ( 67)
#>             > rows only in y  (343)
#>             > matched rows       8
#>             >                 =====
#>             > rows total         8
#> inner_join: added no columns
#>             > rows only in x  (   64)
#>             > rows only in y  (1,335)
#>             > matched rows         2
#>             >                 =======
#>             > rows total           2
#> inner_join: added no columns
#>             > rows only in x  (   59)
#>             > rows only in y  (1,127)
#>             > matched rows         7
#>             >                 =======
#>             > rows total           7
#> inner_join: added no columns
#>             > rows only in x  ( 64)
#>             > rows only in y  (482)
#>             > matched rows       2
#>             >                 =====
#>             > rows total         2
#> inner_join: added no columns
#>             > rows only in x  ( 65)
#>             > rows only in y  (147)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> inner_join: added no columns
#>             > rows only in x  ( 66)
#>             > rows only in y  (109)
#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  (   13)
#>             > rows only in y  (1,336)
#>             > matched rows         1
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (   10)
#>             > rows only in y  (1,130)
#>             > matched rows         4
#>             >                 =======
#>             > rows total           4
#> inner_join: added no columns
#>             > rows only in x  ( 14)
#>             > rows only in y  (484)
#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  ( 13)
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#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> inner_join: added no columns
#>             > rows only in x  ( 14)
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#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  ( 13)
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#>             > matched rows       3
#>             >                 =====
#>             > rows total         3
#> inner_join: added no columns
#>             > rows only in x  ( 15)
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#>             > matched rows       1
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#>             > rows total         1
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#>             > rows only in x  ( 16)
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#>             > matched rows       0
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#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  (16)
#>             > rows only in y  (51)
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#>             > rows total        0
#> inner_join: added no columns
#>             > rows only in x  ( 14)
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#>             > matched rows       2
#>             >                 =====
#>             > rows total         2
#> inner_join: added no columns
#>             > rows only in x  ( 12)
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#>             > matched rows       6
#>             >                 =====
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#> inner_join: added no columns
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#>             > rows only in y  (202)
#>             > matched rows       1
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  ( 18)
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#>             > matched rows       0
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#>             > rows only in x  (16)
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#>             >                 ====
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#> inner_join: added no columns
#>             > rows only in x  ( 16)
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#>             > matched rows       2
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#> inner_join: added no columns
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#>             > matched rows         1
#>             >                 =======
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#> inner_join: added no columns
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#>             >                 =======
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#> inner_join: added no columns
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#> inner_join: added no columns
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#>             >                 =====
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#> inner_join: added no columns
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#>             >                 =======
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#> inner_join: added no columns
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#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (307)
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#>             >                 =====
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#>             > matched rows       1
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (313)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (279)
#>             > rows only in y  (313)
#>             > matched rows      38
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (  289)
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#>             > matched rows        22
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (  248)
#>             > rows only in y  (1,071)
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#>             >                 =======
#>             > rows total          63
#> inner_join: added no columns
#>             > rows only in x  (275)
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#>             > matched rows      36
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (301)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (305)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (430)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (465)
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#>             > matched rows      12
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (476)
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#>             > matched rows       1
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#> inner_join: added no columns
#>             > rows only in x  (472)
#>             > rows only in y  ( 46)
#>             > matched rows       5
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (424)
#>             > rows only in y  (298)
#>             > matched rows      53
#>             >                 =====
#>             > rows total        53
#> inner_join: added no columns
#>             > rows only in x  (  479)
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#>             > matched rows        51
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (  425)
#>             > rows only in y  (1,029)
#>             > matched rows       105
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (482)
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#>             > matched rows      48
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (513)
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#>             > matched rows      17
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (522)
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#>             > matched rows       8
#>             >                 =====
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#> inner_join: added no columns
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#>             > matched rows         4
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#>             >                 =======
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#> inner_join: added no columns
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#> inner_join: added no columns
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#> inner_join: added no columns
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#> inner_join: added no columns
#>             > rows only in x  ( 28)
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#> inner_join: added no columns
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#> inner_join: added no columns
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#> inner_join: added no columns
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#> inner_join: added no columns
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#> inner_join: added no columns
#>             > rows only in x  (  118)
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#>             > matched rows        20
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (  105)
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#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (121)
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#> inner_join: added no columns
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (135)
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#> inner_join: added no columns
#>             > rows only in x  (   73)
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#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (   54)
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#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  ( 69)
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#>             >                 =====
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#> inner_join: added no columns
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  ( 79)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  ( 43)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  ( 47)
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#>             > matched rows       1
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  ( 48)
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#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> inner_join: added no columns
#>             > rows only in x  (46)
#>             > rows only in y  (49)
#>             > matched rows      2
#>             >                 ====
#>             > rows total        2
#> inner_join: added no columns
#>             > rows only in x  ( 42)
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#>             > matched rows       6
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (132)
#>             > rows only in y  (681)
#>             > matched rows      11
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (137)
#>             > rows only in y  (197)
#>             > matched rows       6
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (143)
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#> inner_join: added no columns
#>             > rows only in x  (143)
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#> inner_join: added no columns
#>             > rows only in x  (134)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (  611)
#>             > rows only in y  (1,239)
#>             > matched rows        98
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (539)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (663)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (686)
#>             > rows only in y  (125)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (700)
#>             > rows only in y  (100)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (  525)
#>             > rows only in y  (1,220)
#>             > matched rows       117
#>             >                 =======
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#> inner_join: added no columns
#>             > rows only in x  (506)
#>             > rows only in y  (998)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (596)
#>             > rows only in y  (438)
#>             > matched rows      46
#>             >                 =====
#>             > rows total        46
#> inner_join: added no columns
#>             > rows only in x  (616)
#>             > rows only in y  (122)
#>             > matched rows      26
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (633)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (148)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (160)
#>             > rows only in y  (197)
#>             > matched rows       6
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (166)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (164)
#>             > rows only in y  ( 49)
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#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (157)
#>             > rows only in y  (342)
#>             > matched rows       9
#>             >                 =====
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#> inner_join: added no columns
#>             > rows only in x  (2,114)
#>             > rows only in y  (  998)
#>             > matched rows       339
#>             >                 =======
#>             > rows total         339
#> inner_join: added no columns
#>             > rows only in x  (2,026)
#>             > rows only in y  (  707)
#>             > matched rows       427
#>             >                 =======
#>             > rows total         427
#> inner_join: added no columns
#>             > rows only in x  (2,294)
#>             > rows only in y  (  325)
#>             > matched rows       159
#>             >                 =======
#>             > rows total         159
#> inner_join: added no columns
#>             > rows only in x  (2,394)
#>             > rows only in y  (   89)
#>             > matched rows        59
#>             >                 =======
#>             > rows total          59
#> inner_join: added no columns
#>             > rows only in x  (2,416)
#>             > rows only in y  (   72)
#>             > matched rows        37
#>             >                 =======
#>             > rows total          37
#> inner_join: added no columns
#>             > rows only in x  (1,077)
#>             > rows only in y  (  582)
#>             > matched rows       110
#>             >                 =======
#>             > rows total         110
#> inner_join: added no columns
#>             > rows only in x  (1,159)
#>             > rows only in y  (  175)
#>             > matched rows        28
#>             >                 =======
#>             > rows total          28
#> inner_join: added no columns
#>             > rows only in x  (1,181)
#>             > rows only in y  (  108)
#>             > matched rows         6
#>             >                 =======
#>             > rows total           6
#> inner_join: added no columns
#>             > rows only in x  (1,176)
#>             > rows only in y  (   40)
#>             > matched rows        11
#>             >                 =======
#>             > rows total          11
#> inner_join: added no columns
#>             > rows only in x  (1,121)
#>             > rows only in y  (  285)
#>             > matched rows        66
#>             >                 =======
#>             > rows total          66

# check how well pr improves when going from 2 to 12 months on average
pr %>%
  filter(study %in% c("pure_ccl_12m_vs_0m", "pure_ccl_2m_vs_0m")) %>%
  select(study, regulation, recall, etiology) %>%
  spread(study, recall) %>%
  mutate(diff = pure_ccl_12m_vs_0m - pure_ccl_2m_vs_0m) %>%
  pull(diff) %>%
  mean()
#> filter: removed 100 rows (83%), 20 rows remaining
#> select: dropped 6 variables (class, mm_genes, hs_genes, recall_ratio, precision, …)
#> spread: reorganized (study, recall) into (pure_ccl_2m_vs_0m, pure_ccl_12m_vs_0m) [was 20x4, now 10x4]
#> [1] 0.182263

saveRDS(pr, here(output_path, "precision_recall.rds"))

PR scatter plot

Scatter plot showing precision and recall for each chronic mouse model.

pr <- readRDS(here(output_path, "precision_recall.rds"))

pr %>%
  ggplot(aes(x = recall, y = precision, label = study, color = study)) +
  geom_point() +
  facet_rep_grid(etiology ~ regulation) +
  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()

Version Author Date
3340593 christianholland 2021-02-28

Integration with chronicity categories

PR of chronicity and etiologies

# load etiology gene sets
etiology_genesets <- readRDS(here(output_path, "etiology_gene_sets.rds")) %>%
  mutate(join_var = "xy") %>%
  nest(etiology_genes = -c(etiology, regulation, join_var))

# load exclusive chronic and acute and common genes
chronic <- readRDS(here("output/meta-chronic-vs-acute/ranked_exclusive_chronic_genes.rds")) %>%
  mutate(class = "chronic") %>%
  select(gene, rank, class, statistic = chronic_statistic)
#> select: renamed one variable (statistic) and dropped 4 variables

acute <- readRDS(here("output/meta-chronic-vs-acute/ranked_exclusive_acute_genes.rds")) %>%
  mutate(class = "acute") %>%
  select(gene, rank, class, statistic = acute_statistic)
#> select: renamed one variable (statistic) and dropped 4 variables

common <- readRDS(here("output/meta-chronic-vs-acute/ranked_common_genes.rds")) %>%
  mutate(class = "common") %>%
  select(gene, rank, class, statistic = chronic_statistic)
#> select: renamed one variable (statistic) and dropped 4 variables

chronicity <- bind_rows(chronic, acute, common) %>%
  mutate(regulation = if_else(sign(statistic) >= 0, "up", "down"))

chronicity_hgnc <- chronicity %>%
  translate_gene_ids(from = "symbol_mgi", to = "symbol_hgnc", na_rm = TRUE) %>%
  distinct() %>%
  # drop genes that appear due to translation now in multiple classes
  group_by(gene) %>%
  filter(n() == 1) %>%
  ungroup()
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> rename: renamed one variable (symbol_mgi)
#> left_join: added one column (symbol_hgnc)
#>            > rows only in x      168
#>            > rows only in y  (26,671)
#>            > matched rows      3,790    (includes duplicates)
#>            >                 ========
#>            > rows total        3,958
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 168 rows (4%), 3,790 rows remaining
#> distinct: removed 37 rows (1%), 3,753 rows remaining
#> filter (grouped): removed 173 rows (5%), 3,580 rows remaining
#> ungroup: no grouping variables

# number of genes per regulated chronicity category
chronicity_hgnc %>%
  count(class, regulation) %>%
  ggplot(aes(x = class, y = n, fill = regulation)) +
  geom_col(position = "dodge") +
  geom_hline(yintercept = 120)
#> count: now 6 rows and 3 columns, ungrouped

Version Author Date
3340593 christianholland 2021-02-28

setup <- chronicity_hgnc %>%
  group_by(class, regulation) %>%
  slice_max(order_by = rank, n = 120, with_ties = FALSE) %>%
  mutate(join_var = "xy") %>%
  nest(mm_genes = -c(class, regulation, join_var)) %>%
  ungroup() %>%
  inner_join(etiology_genesets, by = c("regulation", "join_var")) %>%
  select(-join_var)
#> slice_max (grouped): removed 2,860 rows (80%), 720 rows remaining
#> ungroup: no grouping variables
#> inner_join: added 2 columns (etiology, etiology_genes)
#>             > rows only in x  ( 0)
#>             > rows only in y  ( 0)
#>             > matched rows     30    (includes duplicates)
#>             >                 ====
#>             > rows total       30
#> select: dropped one variable (join_var)

pr <- setup %>%
  mutate(pr = map2(etiology_genes, mm_genes, function(hs_genes, mm_genes) {
    z <- inner_join(
      select(mm_genes, gene),
      select(hs_genes, gene),
      by = "gene"
    )

    tibble(
      recall = nrow(z) / nrow(hs_genes),
      recall_ratio = glue("{nrow(z)}/{nrow(hs_genes)}"),
      precision = nrow(z) / nrow(mm_genes),
      precision_ratio = glue("{nrow(z)}/{nrow(mm_genes)}")
    )
  })) %>%
  unnest(pr)
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (112)
#>             > rows only in y  (684)
#>             > matched rows       8
#>             >                 =====
#>             > rows total         8
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (202)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (113)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  ( 50)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (113)
#>             > rows only in y  (344)
#>             > matched rows       7
#>             >                 =====
#>             > rows total         7
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (  113)
#>             > rows only in y  (1,330)
#>             > matched rows         7
#>             >                 =======
#>             > rows total           7
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (  107)
#>             > rows only in y  (1,121)
#>             > matched rows        13
#>             >                 =======
#>             > rows total          13
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (114)
#>             > rows only in y  (478)
#>             > matched rows       6
#>             >                 =====
#>             > rows total         6
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (117)
#>             > rows only in y  (145)
#>             > matched rows       3
#>             >                 =====
#>             > rows total         3
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (120)
#>             > rows only in y  (109)
#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (112)
#>             > rows only in y  (684)
#>             > matched rows       8
#>             >                 =====
#>             > rows total         8
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (202)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (113)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  ( 50)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (113)
#>             > rows only in y  (344)
#>             > matched rows       7
#>             >                 =====
#>             > rows total         7
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (  102)
#>             > rows only in y  (1,319)
#>             > matched rows        18
#>             >                 =======
#>             > rows total          18
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (   96)
#>             > rows only in y  (1,110)
#>             > matched rows        24
#>             >                 =======
#>             > rows total          24
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (111)
#>             > rows only in y  (475)
#>             > matched rows       9
#>             >                 =====
#>             > rows total         9
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (147)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (119)
#>             > rows only in y  (108)
#>             > matched rows       1
#>             >                 =====
#>             > rows total         1
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  ( 96)
#>             > rows only in y  (668)
#>             > matched rows      24
#>             >                 =====
#>             > rows total        24
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (114)
#>             > rows only in y  (197)
#>             > matched rows       6
#>             >                 =====
#>             > rows total         6
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (120)
#>             > rows only in y  (114)
#>             > matched rows       0
#>             >                 =====
#>             > rows total         0
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (117)
#>             > rows only in y  ( 48)
#>             > matched rows       3
#>             >                 =====
#>             > rows total         3
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (105)
#>             > rows only in y  (336)
#>             > matched rows      15
#>             >                 =====
#>             > rows total        15
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (  109)
#>             > rows only in y  (1,326)
#>             > matched rows        11
#>             >                 =======
#>             > rows total          11
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (   84)
#>             > rows only in y  (1,098)
#>             > matched rows        36
#>             >                 =======
#>             > rows total          36
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (110)
#>             > rows only in y  (474)
#>             > matched rows      10
#>             >                 =====
#>             > rows total        10
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (116)
#>             > rows only in y  (144)
#>             > matched rows       4
#>             >                 =====
#>             > rows total         4
#> select: dropped 2 variables (rank, statistic)
#> select: no changes
#> inner_join: added no columns
#>             > rows only in x  (115)
#>             > rows only in y  (104)
#>             > matched rows       5
#>             >                 =====
#>             > rows total         5

saveRDS(pr, here(output_path, "precision_recall_chronicity.rds"))

set.seed(123)
pr %>%
  mutate(regulation = factor(regulation, levels = c("up", "down"))) %>%
  ggplot(aes(
    x = recall, y = precision, label = class,
    color = class
  )) +
  geom_jitter(width = 0.0015, height = 0) +
  facet_rep_grid(etiology ~ regulation) +
  geom_abline(lty = "dashed") +
  expand_limits(x = 0, y = 0) +
  labs(x = "Recall", y = "Precision") +
  my_theme(fsize = fz) +
  scale_color_viridis_d()

Version Author Date
3340593 christianholland 2021-02-28

Comparison of basal expression between mouse and human

# load basal samples from ccl4 mouse model
mouse_control_samples <- readRDS(
  here("data/mouse-chronic-ccl4/meta_data.rds")
) %>%
  filter(time == 0) %>%
  select(sample)
#> filter: removed 30 rows (83%), 6 rows remaining
#> select: dropped 3 variables (time, treatment, group)

mouse_expr <- readRDS(
  here("output/mouse-chronic-ccl4/normalized_expression.rds")
)

mouse_basal_expr <- mouse_expr %>%
  tdy("gene", "sample", "expression") %>%
  inner_join(mouse_control_samples) %>%
  translate_gene_ids(from = "symbol_mgi", to = "symbol_hgnc") %>%
  drop_na() %>%
  # for duplicated genes, keep the one with the highest mean expression
  group_by(sample, gene) %>%
  slice_max(order_by = mean(expression), n = 1, with_ties = F) %>%
  ungroup() %>%
  mutate(organism = "mouse")
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> gather: reorganized (I00001, I00002, I00003, I00004, I00005, …) into (sample, expression) [was 15338x37, now 552168x3]
#> inner_join: added no columns
#>             > rows only in x  (460,140)
#>             > rows only in y  (      0)
#>             > matched rows      92,028
#>             >                 =========
#>             > rows total        92,028
#> rename: renamed one variable (symbol_mgi)
#> left_join: added one column (symbol_hgnc)
#>            > rows only in x   11,496
#>            > rows only in y  (16,094)
#>            > matched rows     86,202    (includes duplicates)
#>            >                 ========
#>            > rows total       97,698
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 11,496 rows (12%), 86,202 rows remaining
#> slice_max (grouped): removed 5,664 rows (7%), 80,538 rows remaining
#> ungroup: no grouping variables

# load basal expression from human
hoang_control_samples <- readRDS(
  here("data/human-hoang-nafld/meta_data.rds")
) %>%
  filter(nafld == "stage_0") %>%
  select(sample)
#> filter: removed 74 rows (95%), 4 rows remaining
#> select: dropped 7 variables (fibrosis, lobular_inflammation, nafld, gender, steatosis, …)

hoang_expr <- readRDS(
  here("output/human-hoang-nafld/normalized_expression.rds")
)

hoang_basal_expr <- hoang_expr %>%
  tdy("gene", "sample", "expression") %>%
  inner_join(hoang_control_samples) %>%
  mutate(organism = "human")
#> gather: reorganized (440349.1.X_1, 440350.1.X_1, 440351.1.X_4, 440352.1.X_4, 440353.1.X_4, …) into (sample, expression) [was 15193x79, now 1185054x3]
#> inner_join: added no columns
#>             > rows only in x  (1,124,282)
#>             > rows only in y  (        0)
#>             > matched rows        60,772
#>             >                 ===========
#>             > rows total          60,772

# combine human and mouse data
all_genes <- bind_rows(mouse_basal_expr, hoang_basal_expr) %>%
  group_by(gene, organism) %>%
  summarise(mean_expression = mean(expression)) %>%
  ungroup() %>%
  pivot_wider(names_from = "organism", values_from = "mean_expression") %>%
  drop_na()
#> summarise: now 28,616 rows and 3 columns, one group variable remaining (gene)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (organism, mean_expression) into (human, mouse) [was 28616x3, now 15819x3]
#> drop_na: removed 3,022 rows (19%), 12,797 rows remaining

consistent_genes <- readRDS(here(
  "output/meta-mouse-vs-human/consistent_genes.rds"
)) %>%
  filter(rank <= 100) %>%
  distinct(gene)
#> filter: removed 2,675 rows (49%), 2,744 rows remaining
#> distinct: removed 2,644 rows (96%), 100 rows remaining

top_consistent_genes <- all_genes %>%
  inner_join(consistent_genes, by = "gene")
#> inner_join: added no columns
#>             > rows only in x  (12,697)
#>             > rows only in y  (     0)
#>             > matched rows        100
#>             >                 ========
#>             > rows total          100

cor.test(all_genes$human, all_genes$mouse, method = "pearson")
#> 
#>  Pearson's product-moment correlation
#> 
#> data:  all_genes$human and all_genes$mouse
#> t = 108.77, df = 12795, p-value < 2.2e-16
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.6840018 0.7020094
#> sample estimates:
#>       cor 
#> 0.6931137
cor.test(top_consistent_genes$human,
  top_consistent_genes$mouse,
  method = "pearson"
)
#> 
#>  Pearson's product-moment correlation
#> 
#> data:  top_consistent_genes$human and top_consistent_genes$mouse
#> t = 10.141, df = 98, p-value < 2.2e-16
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.6040579 0.7996065
#> sample estimates:
#>       cor 
#> 0.7155746

saveRDS(
  list(all = all_genes, top = top_consistent_genes),
  here(output_path, "basel_gene_expression_levels.rds")
)

Time spend to execute this analysis: 03:47 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] gtools_3.8.2             msigdf_7.1               patchwork_1.1.1         
#>  [4] ggwordcloud_0.5.0        UpSetR_1.4.0             ggpubr_0.4.0            
#>  [7] cowplot_1.1.0            gridExtra_2.3            ComplexHeatmap_2.4.3    
#> [10] VennDiagram_1.6.20       futile.logger_1.4.3      lemon_0.4.5             
#> [13] AachenColorPalette_1.1.2 circlize_0.4.11          biobroom_1.20.0         
#> [16] broom_0.7.3              progeny_1.10.0           dorothea_1.0.1          
#> [19] fgsea_1.14.0             janitor_2.0.1            tidytext_0.2.6          
#> [22] glue_1.4.2               readxl_1.3.1             here_1.0.1              
#> [25] tidylog_1.0.2            forcats_0.5.0            stringr_1.4.0           
#> [28] dplyr_1.0.2              purrr_0.3.4              readr_1.4.0             
#> [31] tidyr_1.1.2              tibble_3.0.4             ggplot2_3.3.2           
#> [34] 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] rio_0.5.16           ellipsis_0.3.1       rprojroot_2.0.2     
#>  [7] snakecase_0.11.0     GlobalOptions_0.1.2  fs_1.5.0            
#> [10] clue_0.3-58          rstudioapi_0.13      farver_2.0.3        
#> [13] SnowballC_0.7.0      ggrepel_0.9.0        fansi_0.4.1         
#> [16] lubridate_1.7.9.2    xml2_1.3.2           codetools_0.2-18    
#> [19] knitr_1.30           jsonlite_1.7.2       bcellViper_1.24.0   
#> [22] cluster_2.1.1        dbplyr_2.0.0         png_0.1-7           
#> [25] compiler_4.0.5       httr_1.4.2           backports_1.2.1     
#> [28] assertthat_0.2.1     Matrix_1.3-2         cli_2.2.0           
#> [31] later_1.1.0.1        formatR_1.7          htmltools_0.5.0     
#> [34] tools_4.0.5          gtable_0.3.0         fastmatch_1.1-0     
#> [37] Rcpp_1.0.5           carData_3.0-4        Biobase_2.48.0      
#> [40] cellranger_1.1.0     vctrs_0.3.6          xfun_0.19           
#> [43] openxlsx_4.2.3       rvest_0.3.6          lifecycle_0.2.0     
#> [46] renv_0.12.3          rstatix_0.6.0        scales_1.1.1        
#> [49] clisymbols_1.2.0     hms_0.5.3            promises_1.1.1      
#> [52] parallel_4.0.5       lambda.r_1.2.4       RColorBrewer_1.1-2  
#> [55] curl_4.3             yaml_2.2.1           stringi_1.5.3       
#> [58] tokenizers_0.2.1     BiocGenerics_0.34.0  zip_2.1.1           
#> [61] BiocParallel_1.22.0  shape_1.4.5          rlang_0.4.9         
#> [64] pkgconfig_2.0.3      evaluate_0.14        lattice_0.20-41     
#> [67] labeling_0.4.2       tidyselect_1.1.0     plyr_1.8.6          
#> [70] magrittr_2.0.1       R6_2.5.0             generics_0.1.0      
#> [73] DBI_1.1.0            foreign_0.8-81       pillar_1.4.7        
#> [76] haven_2.3.1          whisker_0.4          withr_2.3.0         
#> [79] abind_1.4-5          car_3.0-10           janeaustenr_0.1.5   
#> [82] modelr_0.1.8         crayon_1.3.4         futile.options_1.0.1
#> [85] rmarkdown_2.6        GetoptLong_1.0.5     data.table_1.13.4   
#> [88] git2r_0.27.1         reprex_0.3.0         digest_0.6.27       
#> [91] httpuv_1.5.4         munsell_0.5.0        viridisLite_0.3.0