Chronic CCl4 (Fig. 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/15-plot-chronic-ccl4.Rmd) and HTML (docs/15-plot-chronic-ccl4.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. |
| Rmd | 591f2bc | christianholland | 2021-02-28 | wflow_publish(c("analysis/*"), delete_cache = TRUE) |
Introduction
Here we generate publication ready plots of the analysis of the chronic CCl4 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(VennDiagram)
library(grid)
library(gridExtra)
library(patchwork)
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/mouse-chronic-ccl4"
output_path <- "output/mouse-chronic-ccl4"
# 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")) +
theme(plot.margin = margin(r = 1, unit = "cm"))Histology
histology <- ggdraw() +
draw_image(here(data_path, "histology.png"))Liver enyzmes
df <- read_csv2(here(data_path, "liver_enzymes.csv")) %>%
mutate(time = parse_number(time),
time = replace_na(time, 0)) %>%
mutate(time = ordered(time)) %>%
pivot_longer(col = -c(time), names_to = "enzyme", values_to = "y") %>%
mutate(enzyme = factor(str_to_upper(enzyme), levels = c("ALT", "AST", "ALP")))
df_summary <- df %>%
group_by(time, enzyme) %>%
summarise(mean_se(y)) %>%
ungroup()
liver_enzymes_partial <- df %>%
ggplot(aes(x = time, y = y)) +
geom_boxplot() +
geom_jitter(alpha = 0.5) +
# geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
# geom_col() +
facet_rep_wrap(~enzyme, scales = "free", ncol = 3) +
labs(x = "Time in month", y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "0", hide.ns = T
)
liver_enzymes <- df %>%
ggplot(aes(x = time, y = y)) +
geom_boxplot() +
geom_jitter(alpha = 0.5) +
# geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5) +
# geom_col() +
facet_rep_wrap(~enzyme, scales = "free", ncol = 1) +
labs(x = "Time in month", y = "U/L") +
my_theme(grid = "y", fsize = fz) +
stat_compare_means(
data = df, label = "p.signif",
ref.group = "0", hide.ns = T
)
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 == "CCl4" & class == "Chronic") %>%
distinct(time = value, label = time_label2) %>%
drop_na() %>%
add_row(time = 0, label = "Control") %>%
mutate(time = ordered(time))
pca_plot <- pca_result$coords %>%
arrange(time) %>%
inner_join(keys, by = "time") %>%
mutate(label = fct_inorder(label)) %>%
mutate(treatment = case_when(treatment == "oil" ~ "Oil",
treatment == "ccl4" ~ "CCl4 + Oil",
treatment == "ctrl" ~ "Control")) %>%
ggplot(aes(x=PC1, y=PC2, color=label, shape = treatment, label = label)) +
geom_point() +
labs(x = paste0("PC1", " (", pca_result$var[1], "%)"),
y = paste0("PC2", " (", pca_result$var[2], "%)"),
color = "Time", shape = "Treatment") +
my_theme(fsize = fz) +
theme(legend.position = "top",
legend.box.margin = margin(10, 0, -20, 10)) +
scale_color_manual(values = aachen_color(c(
"violet", "bordeaux", "red", "orange"
)))
pca_plot
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Volcano plot
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_ccl4") %>%
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))
volcano <- df %>%
plot_volcano(ncol = 1) +
geom_text(
data = filter(deg_count),
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)
volcano
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Top DEGs
df <- readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_ccl4") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2)
top_genes_df <- df %>%
group_by(class, sign(logFC)) %>%
slice_max(order_by = abs(statistic), n = 10, with_ties = F) %>%
ungroup() %>%
nest(data = -c(class, value))
plots <- top_genes_df %>%
mutate(p = pmap(., .f = plot_top_genes, fontsize = fz))
# for main panel
top_genes <- plots %>%
pull(p) %>%
wrap_plots() +
plot_layout(ncol = 1)
top_genes
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Gene overlap
df <- readRDS(here(output_path, "limma_result.rds"))
tables = df %>%
filter(contrast_reference == "pure_ccl4") %>%
mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
select(-contrast_reference, -contrast) %>%
mutate(class = factor(class,
levels = c("Month 2", "Month 6", "Month 12"))) %>%
group_split(class)
# extract labellers
c1 = tables[[1]] %>% distinct(class) %>% pull() %>% as.character()
c2 = tables[[2]] %>% distinct(class) %>% pull() %>% as.character()
c3 = tables[[3]] %>% distinct(class) %>% pull() %>% as.character()
t1 = tables[[1]] %>% count(regulation)
t2 = tables[[2]] %>% count(regulation)
t3 = tables[[3]] %>% count(regulation)
plots = c("up", "down") %>%
map(function(r) {
# set sizes of regulated genes
a1 = t1 %>% filter(regulation == r) %>% pull(n)
a2 = t2 %>% filter(regulation == r) %>% pull(n)
a3 = t3 %>% filter(regulation == r) %>% pull(n)
a12 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a23 = purrr::reduce(
list(tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a13 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
a123 = purrr::reduce(
list(tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)),
intersect) %>%
length()
grid.newpage()
p = draw.triple.venn(
area1 = a1, area2 = a2, area3 = a3,
n12 = a12, n23 = a23, n13 = a13,
n123 = a123,
category = c(c1, c2, c3),
# lty = "blank",
cex = 1/12*fz,
fontfamily = rep("sans", 7),
# fill = aachen_color(c("purple", "petrol", "red")),
# cat.col = aachen_color(c("purple", "petrol", "red")),
cat.cex = 1/12*(fz+1),
cat.fontfamily = rep("sans", 3),
cat.pos = c(350,10,180),
cat.prompts = T,
cat.just = list(c(0.5, 1), c(0.5, 1), c(0.5, 1))
) %>%
as_ggplot() %>%
grid.arrange(top = textGrob(str_to_title(r), gp=gpar(fontsize=fz,
fontface = "bold")))
})
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
gene_overlap = wrap_plots(plots)Top gene of overlap
df = readRDS(here(output_path, "limma_result.rds")) %>%
filter(contrast_reference == "pure_ccl4") %>%
inner_join(key_mm) %>%
select(-class) %>%
rename(class = time_label2) %>%
filter(regulation != "ns") %>%
mutate(regulation = fct_inorder(str_to_title(regulation)))
top_genes_ranked = df %>%
# filter for genes that are deregulated at all time points
group_by(gene, regulation) %>%
filter(n() == 3) %>%
summarise(mean_logfc = mean(logFC)) %>%
group_by(regulation) %>%
mutate(rank = row_number(-abs(mean_logfc))) %>%
ungroup()
top_genes_of_overlap = df %>%
inner_join(top_genes_ranked, by=c("gene", "regulation"))
top_overlap_genes = top_genes_of_overlap %>%
filter(rank <= 5) %>%
ggplot(aes(x=fct_reorder(gene, mean_logfc), y=logFC, group = class,
fill = class)) +
geom_col(position = "dodge") +
facet_rep_wrap(~regulation, ncol = 1, scales = "free") +
my_theme(grid = "y", fsize = fz) +
labs(x="Gene", y="logFC", fill = NULL) +
theme(legend.position = "top",
legend.box.margin = margin(-2, 0, -10, 0)) +
scale_fill_manual(values = aachen_color(c("maygreen", "green", "turquoise"))) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
top_overlap_genes
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Time series cluster
profile_label = tribble(
~profile, ~process,
"STEM ID: 14", "Inflammation",
"STEM ID: 6", "Proliferation",
"STEM ID: 17", NA_character_,
"STEM ID: 12", NA_character_,
"STEM ID: 7", "Metabolism (1)",
"STEM ID: 9", "Metabolism (2)",
"STEM ID: 13", "ECM"
) %>%
mutate(profile = fct_inorder(profile),
process = coalesce(process, profile),
process = fct_inorder(process))
stem_res <- readRDS(here(output_path, "stem_result.rds")) %>%
filter(key == "pure_ccl4") %>%
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)
# 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 Months") +
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),
guide = guide_axis(n.dodge = 1)
)
ts_cluster
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Collage
Figure 1
fig1 <- (design + histology) /
((liver_enzymes | (gene_overlap / plot_spacer()) | top_overlap_genes) +
plot_layout(widths = c(1,2.5,2))) /
ts_cluster +
plot_layout(height = c(1, 1, 1)) +
plot_annotation(tag_levels = list(c("A", "B", "C", "D", "", "E", "F"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig1
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
ggsave(here("figures/Figure 1.pdf"), fig1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 1.png"), fig1,
width = 21, height = 29.7, units = c("cm")
)Partial Figure 1
Supplementary Figure 1.1
sfig1_1 <- pca_plot / (volcano | top_genes) +
plot_annotation(tag_levels = list(c("A", "B", "C"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig1_1
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
ggsave(here("figures/Supplementary Figure 1.1.pdf"), sfig1_1,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 1.1.png"), sfig1_1,
width = 21, height = 29.7, units = c("cm")
)Time spend to execute this analysis: 00:52 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] grid stats graphics grDevices datasets utils methods
#> [8] base
#>
#> other attached packages:
#> [1] patchwork_1.1.1 gridExtra_2.3 VennDiagram_1.6.20
#> [4] futile.logger_1.4.3 ggpubr_0.4.0 lemon_0.4.5
#> [7] cowplot_1.1.0 AachenColorPalette_1.1.2 here_1.0.1
#> [10] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [16] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [19] 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
#> [4] rprojroot_2.0.2 tools_4.0.2 backports_1.2.1
#> [7] R6_2.5.0 DBI_1.1.0 colorspace_2.0-0
#> [10] withr_2.3.0 tidyselect_1.1.0 curl_4.3
#> [13] compiler_4.0.2 git2r_0.27.1 cli_2.2.0
#> [16] rvest_0.3.6 formatR_1.7 xml2_1.3.2
#> [19] labeling_0.4.2 scales_1.1.1 digest_0.6.27
#> [22] foreign_0.8-81 rmarkdown_2.6 rio_0.5.16
#> [25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_2.0.0
#> [28] rlang_0.4.9 readxl_1.3.1 rstudioapi_0.13
#> [31] farver_2.0.3 generics_0.1.0 jsonlite_1.7.2
#> [34] 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
#> [40] fansi_0.4.1 abind_1.4-5 lifecycle_0.2.0
#> [43] stringi_1.5.3 whisker_0.4 yaml_2.2.1
#> [46] carData_3.0-4 plyr_1.8.6 promises_1.1.1
#> [49] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
#> [52] hms_0.5.3 magick_2.5.2 knitr_1.30
#> [55] pillar_1.4.7 ggsignif_0.6.0 codetools_0.2-18
#> [58] clisymbols_1.2.0 futile.options_1.0.1 reprex_0.3.0
#> [61] glue_1.4.2 evaluate_0.14 lambda.r_1.2.4
#> [64] data.table_1.13.4 renv_0.12.3 modelr_0.1.8
#> [67] vctrs_0.3.6 httpuv_1.5.4 cellranger_1.1.0
#> [70] gtable_0.3.0 assertthat_0.2.1 xfun_0.19
#> [73] openxlsx_4.2.3 broom_0.7.3 rstatix_0.6.0
#> [76] later_1.1.0.1 ellipsis_0.3.1