Acute APAP (Fig. 2)
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/16-plot-acute-apap.Rmd) and HTML (docs/16-plot-acute-apap.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 publication ready plots of the analysis of the acute APAP mouse model.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(AachenColorPalette)
library(cowplot)
library(lemon)
library(ggpubr)
library(patchwork)
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/mouse-acute-apap"
output_path <- "output/mouse-acute-apap"
# graphical parameters
# fontsize
fz <- 9
# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))Design
design <- ggdraw() +
draw_image(here(data_path, "exp-design.pdf"))Histology
histology <- ggdraw() +
draw_image(here(data_path, "histology.png"))Liver enyzmes
df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
mutate(time = fct_inorder(time)) %>%
pivot_longer(col = -c(time, enzyme), names_to = "mouse", values_to = "y") %>%
mutate(enzyme = factor(str_to_upper(enzyme), levels = c("ALT", "AST")))
df_summary <- df %>%
group_by(time, enzyme) %>%
summarise(mean_se(y)) %>%
ungroup()
liver_enzymes <- df %>%
ggplot(aes(x = time, y = y)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, size = 1) +
# geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
# geom_col() +
facet_rep_wrap(~enzyme, scales = "free_y", ncol = 1) +
labs(x = "Time after APAP injection", y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "Control", hide.ns = T
) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
theme(legend.position = "none",
panel.spacing.y = unit(-2, "lines")) #+
scale_color_manual(values = aachen_color(c(
"blue", "purple", "violet",
"bordeaux", "red", "orange",
"maygreen", "green", "turquoise",
"petrol"
)))
#> <ggproto object: Class ScaleDiscrete, Scale, gg>
#> aesthetics: colour
#> axis_order: function
#> break_info: function
#> break_positions: function
#> breaks: waiver
#> call: call
#> clone: function
#> dimension: function
#> drop: TRUE
#> expand: waiver
#> get_breaks: function
#> get_breaks_minor: function
#> get_labels: function
#> get_limits: function
#> guide: legend
#> is_discrete: function
#> is_empty: function
#> labels: waiver
#> limits: NULL
#> make_sec_title: function
#> make_title: function
#> map: function
#> map_df: function
#> n.breaks.cache: NULL
#> na.translate: TRUE
#> na.value: NA
#> name: waiver
#> palette: function
#> palette.cache: NULL
#> position: left
#> range: <ggproto object: Class RangeDiscrete, Range, gg>
#> range: NULL
#> reset: function
#> train: function
#> super: <ggproto object: Class RangeDiscrete, Range, gg>
#> rescale: function
#> reset: function
#> scale_name: manual
#> train: function
#> train_df: function
#> transform: function
#> transform_df: function
#> super: <ggproto object: Class ScaleDiscrete, Scale, gg>
liver_enzymes
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
PCA
pca_result <- readRDS(here(output_path, "pca_result.rds"))
keys <- key_mm %>%
filter(treatment == "APAP" & class == "Acute") %>%
distinct(time = value, label = time_label2) %>%
drop_na() %>%
add_row(time = 0, label = "Control") %>%
mutate(time = ordered(time))
pca_plot <- pca_result$coords %>%
arrange(time) %>%
inner_join(keys, by = "time") %>%
mutate(label = fct_inorder(label)) %>%
ggplot(aes(x = PC1, y = PC2, color = label, label = label)) +
geom_point() +
labs(
x = paste0("PC1", " (", pca_result$var[1], "%)"),
y = paste0("PC2", " (", pca_result$var[2], "%)"),
color = "Time"
) +
my_theme(fsize = fz) +
theme(legend.position = "top",
legend.box.margin = margin(10, 0, -20, 0),
legend.title = element_blank()) +
scale_color_manual(values = aachen_color(c(
"blue", "purple", "violet",
"bordeaux", "red", "orange",
"maygreen", "green", "turquoise",
"petrol"
))) +
guides(col = guide_legend(nrow = 4))
pca_plot
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Volcano plot
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "apap") %>%
inner_join(key_mm, by = "contrast") %>%
select(-contrast) %>%
rename(contrast = time_label2) %>%
mutate(contrast = fct_drop(contrast)) %>%
mutate(regulation = fct_recode(regulation,
Up = "up", Down = "down",
n.s. = "ns"
))
deg_count <- df %>%
add_count(contrast, regulation) %>%
filter(regulation != "n.s.") %>%
mutate(regulation = fct_drop(regulation)) %>%
mutate(
logFC = case_when(
regulation == "Up" ~ 0.75 * max(logFC),
regulation == "Down" ~ 0.75 * min(logFC)
),
pval = 0.4
) %>%
distinct(n, contrast, logFC, pval, regulation, value) %>%
complete(contrast, nesting(regulation, logFC, pval), fill = list(n = 0))
main_time <- c(12, 24, 48)
# for main panel
volcano_main <- df %>%
filter(value %in% main_time) %>%
plot_volcano(nrow = 1) +
geom_text(
data = filter(deg_count, value %in% main_time),
aes(y = pval, label = n), size = fz / (14 / 5),
hjust = "inward", vjust = "inward",
show.legend = F
) +
theme(
legend.position = "top",
legend.box.margin = margin(-10, 0, -10, 0)
) +
labs(color = "Regulation") +
my_theme(grid = "y", fsize = fz)
# for supp panel
volcano_supp <- df %>%
filter(!value %in% main_time) %>%
plot_volcano(ncol = 1) +
geom_text(
data = filter(deg_count, !value %in% main_time),
aes(y = pval, label = n), size = fz / (14 / 5),
show.legend = F
) +
theme(
legend.position = "top",
legend.box.margin = margin(-10, 0, -10, 0)
) +
labs(color = "Regulation") +
my_theme(grid = "y", fsize = fz)
volcano_main
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
volcano_supp
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "apap") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2)
top_genes_df <- df %>%
filter(regulation != "ns") %>%
group_by(class, sign(logFC)) %>%
slice_max(order_by = abs(logFC), n = 5, with_ties = F) %>%
ungroup() %>%
nest(data = -c(class, value))
plots <- top_genes_df %>%
mutate(p = pmap(., .f = plot_top_genes, fontsize = fz-2))
main_time <- c(12, 24, 48)
# for main panel
top_genes_main <- plots %>%
filter(value %in% main_time) %>%
pull(p) %>%
wrap_plots() +
plot_layout(nrow = 1)
# for supp panel
top_genes_supp <- plots %>%
filter(!value %in% main_time) %>%
pull(p) %>%
wrap_plots() +
plot_layout(ncol = 1)
top_genes_main
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
top_genes_supp
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Time series cluster
profile_label <- tribble(
~profile, ~process,
"STEM ID: 14", "Stress resp.",
"STEM ID: 9", "Migration",
"STEM ID: 12", "Proliferation",
"STEM ID: 6", "Metabolism 1",
"STEM ID: 11", "Metabolism 2",
"STEM ID: 18", "Metabolism 3",
"STEM ID: 8", "Metabolism 4"
) %>%
mutate(
profile = fct_inorder(profile),
process = coalesce(process, profile),
process = fct_inorder(process)
)
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "apap") %>%
filter(p <= 0.05) %>%
mutate(profile = fct_reorder(str_c(
"STEM ID: ",
as.character(profile)
), p)) %>%
inner_join(profile_label, by = "profile") %>%
select(-profile) %>%
rename(profile = process) %>%
filter(!time %in% c(384, 192, 144))
# extract meta data of profiles
profile_anno <- stem_res %>%
group_by(key, profile, p) %>%
mutate(y = 1.2 * abs(max(value))) %>%
ungroup() %>%
mutate(max_time = max(time)) %>%
distinct(key, profile, p, size, y, max_time) %>%
mutate(label = str_c(size, " ", "genes"))
ts_cluster <- stem_res %>%
plot_stem_profiles(model_profile = F, nrow = 2) +
labs(x = "Time in hours") +
geom_text(
data = profile_anno, aes(x = 0, y = y, label = label),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward"
) +
my_theme(grid = "no", fsize = fz) +
scale_x_continuous(
breaks = unique(stem_res$time),
labels = c("", 1,"", "",24,"", 96)
)
ts_cluster
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Collage
Figure 2
fig2 = (((design / liver_enzymes) | histology) + plot_layout(width = c(1,2.5))) /
((pca_plot | (volcano_main / top_genes_main)) + plot_layout(width = c(1,2))) /
ts_cluster +
plot_layout(height = c(2, 1, 1)) +
plot_annotation(tag_levels = list(c("A", "C", "B", "D", "E", "F", "", "", "",
"", "","G"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig2
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
ggsave(here("figures/Figure 2.pdf"), fig2,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 2.png"), fig2,
width = 21, height = 29.7, units = c("cm")
)Partial Figure 2
Supplementary Figure 2.1
sfig2_1 <- (volcano_supp | top_genes_supp) +
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")
)
sfig2_1
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
ggsave(here("figures/Supplementary Figure 2.1.pdf"), sfig2_1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 2.1.png"), sfig2_1,
width = 21, height = 29.7, units = c("cm")
)Time spend to execute this analysis: 01:28 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 ggpubr_0.4.0 lemon_0.4.5
#> [4] cowplot_1.1.0 AachenColorPalette_1.1.2 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] fs_1.5.0 lubridate_1.7.9.2 httr_1.4.2 rprojroot_2.0.2
#> [5] tools_4.0.2 backports_1.2.1 R6_2.5.0 DBI_1.1.0
#> [9] colorspace_2.0-0 withr_2.3.0 tidyselect_1.1.0 gridExtra_2.3
#> [13] curl_4.3 compiler_4.0.2 git2r_0.27.1 cli_2.2.0
#> [17] rvest_0.3.6 xml2_1.3.2 labeling_0.4.2 scales_1.1.1
#> [21] digest_0.6.27 foreign_0.8-81 rmarkdown_2.6 rio_0.5.16
#> [25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_2.0.0 rlang_0.4.9
#> [29] readxl_1.3.1 rstudioapi_0.13 generics_0.1.0 farver_2.0.3
#> [33] jsonlite_1.7.2 gtools_3.8.2 zip_2.1.1 car_3.0-10
#> [37] magrittr_2.0.1 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
#> [41] abind_1.4-5 lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
#> [45] yaml_2.2.1 carData_3.0-4 plyr_1.8.6 grid_4.0.2
#> [49] promises_1.1.1 crayon_1.3.4 lattice_0.20-41 haven_2.3.1
#> [53] hms_0.5.3 magick_2.5.2 knitr_1.30 pillar_1.4.7
#> [57] ggsignif_0.6.0 codetools_0.2-18 clisymbols_1.2.0 reprex_0.3.0
#> [61] glue_1.4.2 evaluate_0.14 data.table_1.13.4 renv_0.12.3
#> [65] modelr_0.1.8 vctrs_0.3.6 httpuv_1.5.4 cellranger_1.1.0
#> [69] gtable_0.3.0 assertthat_0.2.1 xfun_0.19 openxlsx_4.2.3
#> [73] broom_0.7.3 rstatix_0.6.0 later_1.1.0.1 ellipsis_0.3.1