Study overview (SFig. 0.1)
Last updated: 2020-12-23
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Knit directory: meta-liver/
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| Rmd | e8798d3 | christianholland | 2020-12-21 | added plotting script |
Introduction
Here we generate plots display various features of all included studies.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(glue)
library(AachenColorPalette)
library(scales)
library(circlize)
library(patchwork)
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# graphical parameters
# fontsize
fz <- 7
# color function for heatmaps
col_fun <- colorRamp2(
c(-4, 0, 4),
c(aachen_color("blue"), "white", aachen_color("red"))
)
# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))
key_hs <- readRDS(here("data/meta-mouse-vs-human/contrast_annotation.rds"))Studied individuals
Mouse models
df <- readRDS(here("output/meta-chronic-vs-acute/meta_data.rds")) %>%
count(treatment, source, class, group) %>%
mutate(label = case_when(
treatment == "apap" ~ "APAP",
treatment == "bdl" ~ "BDL",
treatment == "ccl4" ~ "CCl4 (Acute)",
treatment == "lps" ~ "LPS",
treatment == "ph" ~ "PH",
treatment == "tunicamycin" ~ "Tunicamycin",
treatment == "pure_ccl4" ~ "CCl4 (Chronic)"
)) %>%
mutate(group = str_to_title(group))
stitle <- df %>%
group_by(group) %>%
tally(n) %>%
mutate(total = sum(n)) %>%
pivot_wider(names_from = group, values_from = n) %>%
mutate(label = glue("Total: {total} ({Control}/{Treated})")) %>%
pull(label)
num_mouse <- df %>%
ggplot(aes(
x = n, fct_reorder(label, n),
group = group, fill = group
)) +
geom_col(position = "dodge") +
labs(
x = "Number of mice", y = "Study", subtitle = stitle,
fill = NULL
) +
my_theme(grid = "x", fsize = fz) +
scale_fill_manual(values = aachen_color(c("blue75", "red75")))
num_mouse
Patient cohorts
keys <- key_hs %>%
distinct(source, phenotype, author2)
df <- readRDS(here("output/meta-mouse-vs-human/meta_data.rds")) %>%
inner_join(keys) %>%
count(author2, class) %>%
mutate(class = str_to_title(class))
stitle <- df %>%
group_by(class) %>%
tally(n) %>%
mutate(total = sum(n)) %>%
pivot_wider(names_from = class, values_from = n) %>%
mutate(label = glue("Total: {total} ({Control}/{Disease})")) %>%
pull(label)
num_patient <- df %>%
ggplot(aes(
x = n, fct_reorder(author2, n),
group = class, fill = class
)) +
geom_col(position = "dodge") +
labs(
x = "Number of patients", y = "Study", subtitle = stitle,
fill = NULL
) +
my_theme(grid = "x", fsize = fz) +
scale_fill_manual(values = aachen_color(c("blue75", "red75")))
num_patient
Gene coverage
Mouse models
keys <- key_mm %>%
distinct(contrast, treatment_abbr, class)
mm <- readRDS(here("output/meta-chronic-vs-acute/limma_result.rds")) %>%
select(-treatment, -class) %>%
inner_join(keys, by = "contrast") %>%
distinct(gene, treatment_abbr, class) %>%
count(treatment_abbr, class) %>%
mutate(group = case_when(
str_detect(treatment_abbr, "CCl4") ~ str_c(treatment_abbr, " (", class, ")"),
TRUE ~ as.character(treatment_abbr)
))
gene_coverage_mm <- mm %>%
ggplot(aes(x = n, fct_reorder(group, n), group = class)) +
geom_col() +
geom_text(aes(x = n, y = fct_reorder(group, n), label = n),
size = (fz - 2) / (14 / 5), color = "white", hjust = 1.5
) +
labs(x = "Gene coverage", y = NULL) +
my_theme(grid = "x", fsize = fz) +
theme(
legend.position = "top",
axis.line = element_blank(),
axis.ticks = element_blank()
) +
scale_x_continuous(labels = label_number_si())
gene_coverage_mm
Patient cohorts
keys <- key_hs %>%
distinct(contrast, source, phenotype, author2)
hs <- readRDS(here("output/meta-mouse-vs-human/limma_result.rds")) %>%
inner_join(keys) %>%
distinct(gene, author2) %>%
count(author2)
gene_coverage_hs <- hs %>%
ggplot(aes(x = n, fct_reorder(author2, n))) +
geom_col() +
geom_text(aes(x = n, y = fct_reorder(author2, n), label = n),
size = (fz - 2) / (14 / 5), color = "white", hjust = 1.5
) +
labs(x = "Gene coverage", y = NULL) +
my_theme(grid = "x", fsize = fz) +
theme(
legend.position = "top",
axis.line = element_blank(),
axis.ticks = element_blank()
) +
scale_x_continuous(labels = label_number_si())
gene_coverage_hs
Collage
Supplementary Figure 0.1
sfig0_1 <- (num_mouse + num_patient) /
(gene_coverage_mm + gene_coverage_hs) +
plot_annotation(tag_levels = list(c("A", "B", "C", "D"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig0_1
| Version | Author | Date |
|---|---|---|
| 35ed693 | christianholland | 2020-12-22 |
ggsave(here("figures/Supplementary Figure 0.1.pdf"), sfig0_1,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 0.1.png"), sfig0_1,
width = 21, height = 10, units = c("cm")
)Time spend to execute this analysis: 00:06 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices datasets utils methods base
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#> other attached packages:
#> [1] patchwork_1.1.1 circlize_0.4.11 scales_1.1.1
#> [4] AachenColorPalette_1.1.2 glue_1.4.2 here_1.0.1
#> [7] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
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