Precision and recall of chronic mouse models (Fig. 5)
Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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Introduction
Here we generate publication-ready plots for the integration of further chronic mouse models.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(AachenColorPalette)
library(scales)
library(UpSetR)
library(grid)
library(lemon)
library(ggrepel)
library(magick)
library(patchwork)
library(gtools)
source(here("code/utils-plots.R"))Definition of global variables and functions 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
# 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"))Chronic mouse model genes in number
keys <- key_mm %>%
distinct(study = contrast, label2)
df <- readRDS(here(output_path, "chronic_mouse_deg_numbers.rds")) %>%
left_join(keys, by = "study") %>%
mutate(label2 = coalesce(label2, study)) %>%
mutate(regulation = fct_rev(str_to_title(regulation)))
num_genes <- df %>%
ggplot(aes(y = fct_reorder(label2, n, sum), x = n, fill = regulation)) +
geom_col(position = "dodge") +
labs(
x = "Number of differentially expressed genes",
y = "Chronic mouse model",
fill = "Regulation"
) +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", scales::math_format(10^.x))
) +
my_theme(grid = "x", fsize = fz) +
scale_fill_manual(values = aachen_color(c("red75", "blue75"))) +
annotation_logticks(sides = "b")
num_genes
Similarity of chronic mouse models and patient cohorts
contrast_keys_human <- key_hs %>%
distinct(contrast, label, source, phenotype) %>%
unite(contrast, source, phenotype, contrast, sep = "-")
contrast_keys_mouse <- key_mm %>%
filter(str_detect(contrast, "pure")) %>%
distinct(contrast, label)
keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)
o <- readRDS(here(output_path, "cross_species_similarity.rds"))
oo <- o %>%
rename(contrast = set1) %>%
left_join(keys, by = "contrast") %>%
mutate(label = coalesce(label, contrast)) %>%
rename(set1 = label, contrast = set2, contrast_set1 = contrast) %>%
left_join(keys, by = "contrast") %>%
mutate(label = coalesce(label, contrast)) %>%
rename(set2 = label, contrast_set2 = contrast) %>%
mutate(
contrast_set1 = factor(contrast_set1, levels = levels(o$set1)),
contrast_set2 = factor(contrast_set2, levels = levels(o$set2)),
set1 = fct_reorder(set1, as.numeric(contrast_set1)),
set2 = fct_reorder(set2, as.numeric(contrast_set2))
) %>%
select(set1, set2, similarity)
gs_sim <- oo %>%
filter(!str_detect(set1, "NAFLD|NASH|PSC|PBC|HCV")) %>%
filter(!str_detect(set2, "WTD|HF|PTEN|MCD|STZ|CCl4")) %>%
ggplot(aes(
x = set1, y = set2, fill = similarity,
label = round(similarity, 3)
)) +
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),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
my_theme(fsize = fz, grid = "no")
gs_sim
Enrichment of chronic mouse genes in patient cohorts
contrast_keys_human <- key_hs %>%
distinct(contrast, label, source) %>%
unite(contrast, source, contrast)
contrast_keys_mouse <- key_mm %>%
filter(str_detect(contrast, "pure")) %>%
distinct(contrast, label)
keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)
gsea_res <- readRDS(here(output_path, "cross_species_enrichment.rds")) %>%
rename(contrast = signature) %>%
inner_join(keys, by = "contrast") %>%
select(-contrast) %>%
rename(signature = label, contrast = geneset) %>%
left_join(keys, by = "contrast") %>%
mutate(label = coalesce(label, contrast)) %>%
select(-contrast) %>%
rename(geneset = label) %>%
mutate(direction = fct_rev(str_to_title(direction)))
tile_up <- gsea_res %>%
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) +
my_theme(grid = "no", fsize = fz) +
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"), limits = c(-1, 1)
) +
my_theme(grid = "no", fsize = fz) +
labs(x = "Signature", y = "Gene Set", fill = "ES") +
guides(fill = guide_colorbar(title = "ES"))
box_up <- gsea_res %>%
filter(direction == "Up") %>%
ggplot(aes(y = geneset, x = ES)) +
geom_boxplot() +
facet_rep_wrap(~direction, ncol = 1) +
geom_vline(xintercept = 0) +
my_theme(fsize = fz, grid = "x") +
theme(
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.y = element_blank()
)
tile_down <- gsea_res %>%
filter(direction == "Down") %>%
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) +
my_theme(grid = "no", fsize = fz) +
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"), limits = c(-1, 1)
) +
my_theme(grid = "no", fsize = fz) +
labs(x = "Signature", y = "Gene Set", fill = "ES") +
guides(fill = guide_colorbar(title = "ES"))
box_down <- gsea_res %>%
filter(direction == "Down") %>%
ggplot(aes(y = geneset, x = ES)) +
geom_boxplot() +
facet_rep_wrap(~direction, ncol = 1) +
geom_vline(xintercept = 0) +
my_theme(fsize = fz, grid = "x") +
theme(
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.y = element_blank()
)
tile_up + box_up
tile_down + box_down
Etiology genes in numbers
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)
pdf(file = here("figures/tmp/Fig5A1.pdf"), width = 15, height = 10, onefile = F)
upset(mat_up,
nintersects = NA, mainbar.y.label = "Common genes",
sets.x.label = "Total number of genes",
text.scale = 3, point.size = 5
)
grid.text("Up", x = 0.65, y = 0.95, gp = gpar(fontsize = fz * 4))
dev.off()
#> quartz_off_screen
#> 2
mat_down <- df %>%
filter(regulation == "down") %>%
select(-regulation) %>%
mutate(val = 1) %>%
spread(etiology, val, fill = 0) %>%
data.frame(row.names = 1)
pdf(file = here("figures/tmp/Fig5A2.pdf"), width = 15, height = 10, onefile = F)
upset(mat_down,
nintersects = NA, mainbar.y.label = "Common genes",
sets.x.label = "Total number of genes",
text.scale = 3, point.size = 5
)
grid.text("Down", x = 0.65, y = 0.95, gp = gpar(fontsize = fz * 4))
dev.off()
#> quartz_off_screen
#> 2PR scatter plot of chronic mouse models
keys <- key_mm %>%
distinct(study = contrast, label2)
pr <- readRDS(here(output_path, "precision_recall.rds")) %>%
left_join(keys, by = "study") %>%
mutate(label2 = coalesce(label2, study)) %>%
mutate(regulation = fct_rev(str_to_title(regulation))) %>%
mutate(x = case_when(
str_detect(label2, "12 Month") ~ as.character(label2),
TRUE ~ NA_character_
)) %>%
mutate(etiology = factor(etiology, levels = c(
"NAFLD", "NASH", "HCV", "PSC",
"PBC"
))) %>%
mutate(label2 = case_when(
str_detect(label2, "HF12") ~ "High-fat diet (12 Weeks)",
str_detect(label2, "HF18") ~ "High-fat diet (18 Weeks)",
str_detect(label2, "HF30") ~ "High-fat diet (30 Weeks)",
str_detect(label2, "STZ12") ~ "Streptozocin diet (12 Weeks)",
str_detect(label2, "STZ18") ~ "Streptozocin diet (18 Weeks)",
str_detect(label2, "MCD4") ~ "Methionine- and choline-deficient diet (4 Weeks)",
str_detect(label2, "MCD8") ~ "Methionine- and choline-deficient diet (8 Weeks)",
str_detect(label2, "WTD") ~ "Western-type diet (12 Weeks)",
str_detect(label2, "PTEN") ~ "PTEN knockout mice",
TRUE ~ as.character(label2)
)) %>%
mutate(label2 = factor(label2, levels = c(
"CCl4 (2 Months)", "CCl4 (6 Months)", "CCl4 (12 Months)",
"High-fat diet (12 Weeks)", "High-fat diet (18 Weeks)",
"High-fat diet (30 Weeks)", "Streptozocin diet (12 Weeks)",
"Streptozocin diet (18 Weeks)",
"Methionine- and choline-deficient diet (4 Weeks)",
"Methionine- and choline-deficient diet (8 Weeks)",
"Western-type diet (12 Weeks)",
"PTEN knockout mice"
)))
pr_plot_mm <- pr %>%
ggplot(aes(x = recall, y = precision, label = x, color = label2)) +
geom_point() +
facet_rep_grid(regulation ~ etiology) +
geom_text_repel(size = fz / (14 / 5), show.legend = FALSE, na.rm = TRUE) +
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(direction = -1, option = "B") +
theme(legend.position = "top") +
guides(col = guide_legend(nrow = 4))
pr_plot_mm
PR scatter plot of chronicity categories
pr <- readRDS(here(output_path, "precision_recall_chronicity.rds")) %>%
mutate(
etiology = factor(etiology, levels = c("NAFLD", "NASH", "HCV", "PSC", "PBC")),
regulation = factor(str_to_title(regulation), levels = c("Up", "Down")),
class = case_when(
str_detect(class, "acute") ~ "Exclusive acute",
str_detect(class, "chronic") ~ "Exclusive chronic",
str_detect(class, "common") ~ "Common in chronic and acute"
),
class = factor(class, levels = c(
"Exclusive chronic",
"Exclusive acute",
"Common in chronic and acute"
))
)
set.seed(123)
pr_plot_category <- pr %>%
ggplot(aes(
x = recall, y = precision, label = class,
color = class
)) +
geom_jitter(width = 0.0015, height = 0) +
facet_rep_grid(regulation ~ etiology) +
geom_abline(lty = "dashed") +
expand_limits(x = 0, y = 0) +
labs(x = "Recall", y = "Precision", color = "Category") +
my_theme(fsize = fz) +
scale_color_manual(values = aachen_color(c("bordeaux", "orange", "petrol"))) +
theme(legend.position = "top")
pr_plot_category
Collage
Figure 5
Main Figure.
upset_up <- image_ggplot(image_read_pdf(here("figures/tmp/Fig5A1.pdf")),
interpolate = TRUE
)
# file.remove(here("figures/tmp/Fig5A1.pdf"))
upset_down <- image_ggplot(image_read_pdf(here("figures/tmp/Fig5A2.pdf")),
interpolate = TRUE
)
# file.remove(here("figures/tmp/Fig5A2.pdf"))
fig5 <- (upset_up | upset_down) /
((pr_plot_mm / pr_plot_category) + plot_layout(heights = c(1, 1))) +
plot_layout(height = c(1, 3)) +
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")
)
fig5
ggsave(here("figures/Figure 5.pdf"), fig5,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 5.png"), fig5,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 5.1
sfig5_1 <- ((num_genes | gs_sim) + plot_layout(width = c(1, 2))) +
# ((tile_up + box_up) + plot_layout(width = c(4, 1), guides = "collect")) /
# ((tile_down + box_down) + plot_layout(width = c(4, 1), guides = "collect")) +
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")
)
sfig5_1
ggsave(here("figures/Supplementary Figure 5.1.pdf"), sfig5_1,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 5.1.png"), sfig5_1,
width = 21, height = 10, units = c("cm")
)Time spend to execute this analysis: 00:51 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] gtools_3.8.2 patchwork_1.1.1 magick_2.5.2
#> [4] ggrepel_0.9.0 lemon_0.4.5 UpSetR_1.4.0
#> [7] scales_1.1.1 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] httr_1.4.2 viridisLite_0.3.0 jsonlite_1.7.2 modelr_0.1.8
#> [5] pdftools_2.3.1 assertthat_0.2.1 askpass_1.1 renv_0.12.3
#> [9] cellranger_1.1.0 yaml_2.2.1 qpdf_1.1 pillar_1.4.7
#> [13] backports_1.2.1 lattice_0.20-41 glue_1.4.2 digest_0.6.27
#> [17] promises_1.1.1 rvest_0.3.6 colorspace_2.0-0 cowplot_1.1.0
#> [21] htmltools_0.5.0 httpuv_1.5.4 plyr_1.8.6 clisymbols_1.2.0
#> [25] pkgconfig_2.0.3 broom_0.7.3 haven_2.3.1 whisker_0.4
#> [29] later_1.1.0.1 git2r_0.27.1 generics_0.1.0 farver_2.0.3
#> [33] ellipsis_0.3.1 withr_2.3.0 cli_2.2.0 magrittr_2.0.1
#> [37] crayon_1.3.4 readxl_1.3.1 evaluate_0.14 fs_1.5.0
#> [41] fansi_0.4.1 xml2_1.3.2 tools_4.0.2 hms_0.5.3
#> [45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 compiler_4.0.2
#> [49] rlang_0.4.9 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.6
#> [53] gtable_0.3.0 codetools_0.2-16 DBI_1.1.0 R6_2.5.0
#> [57] gridExtra_2.3 lubridate_1.7.9.2 knitr_1.30 rprojroot_2.0.2
#> [61] stringi_1.5.3 Rcpp_1.0.5 vctrs_0.3.6 dbplyr_2.0.0
#> [65] tidyselect_1.1.0 xfun_0.19