Study overview (SFig. 0.1)
Last updated: 2021-03-29
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Knit directory: liver-disease-atlas/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/20-plot-study-overview.Rmd) and HTML (docs/20-plot-study-overview.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 24c0c74 | christianholland | 2021-02-28 | Build site. |
| html | 5e36b25 | christianholland | 2021-02-28 | Build site. |
| Rmd | 7f331d0 | christianholland | 2021-02-28 | wflow_publish("analysis/*", delete_cache = TRUE, republish = TRUE) |
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 = "Mouse model", subtitle = stitle,
fill = NULL
) +
my_theme(grid = "x", fsize = fz) +
scale_fill_manual(values = aachen_color(c("blue75", "red75"))) +
scale_y_discrete(labels = c("Tunicamycin","LPS","BDL",
expression(CCl[4] (Chronic)),
expression(CCl[4] (Acute)), "APAP", "PH"))
num_mouse
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
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 = "Patient cohort", subtitle = stitle,
fill = NULL
) +
my_theme(grid = "x", fsize = fz) +
scale_fill_manual(values = aachen_color(c("blue75", "red75")))
num_patient
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
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
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
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
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
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"))) &
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 |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
ggsave(here("figures/Supplementary Figure 0.1.pdf"), sfig0_1,
width = 21, height = 5, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 0.1.png"), sfig0_1,
width = 21, height = 5, units = c("cm")
)Time spend to execute this analysis: 00:08 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
#>
#> 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
#> [10] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [16] tidyverse_1.3.0 workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 lubridate_1.7.9.2 clisymbols_1.2.0
#> [4] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27
#> [7] R6_2.5.0 cellranger_1.1.0 backports_1.2.1
#> [10] reprex_0.3.0 evaluate_0.14 httr_1.4.2
#> [13] pillar_1.4.7 GlobalOptions_0.1.2 rlang_0.4.9
#> [16] readxl_1.3.1 rstudioapi_0.13 whisker_0.4
#> [19] rmarkdown_2.6 labeling_0.4.2 munsell_0.5.0
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