vignettes/articles/FGSEA_heatmaps.Rmd
FGSEA_heatmaps.RmdThis article generates heatmaps of the FGSEA results (Fig. 3C, D, E, G; Extended Data Figs. 3A-D, 5A-D, F).
# Required packages
library(MotrpacRatTraining6moWATData)
library(MotrpacRatTraining6moWAT)
library(ComplexHeatmap)
library(grid)
library(dplyr)
library(purrr)
save_plots <- dir.exists(paths = file.path("..", "..", "plots"))
omes <- c("METAB", "PROT", "TRNSCRPT")
for (ome in omes) {
file <- paste0(ome, "_FGSEA")
fgsea_res <- get(file)
if (ome == "METAB") {
fgsea_res <- map(fgsea_res,
~ mutate(.x, gs_subcat = "refmet_sub_class",
pathway = refmet_sub_class,
gs_description = refmet_sub_class))
}
if (ome %in% c("PROT", "TRNSCRPT")) {
heatmap_legend_side <- annotation_legend_side <- "bottom"
align_heatmap_legend <- align_annotation_legend <- "global_center"
direction <- "horizontal"
legend_width <- unit(90, "points")
legend_height <- NULL
} else {
heatmap_legend_side <- annotation_legend_side <- "right"
align_heatmap_legend <- align_annotation_legend <- "heatmap_top"
direction <- "vertical"
legend_width <- NULL
legend_height <- unit(90, "points")
}
# Enrichment heatmaps ------------------------------
scale <- 2
# Trained vs. SED and sex by training interaction (MvF_exercise_response)
res_list <- fgsea_res[c(1, 3)]
subcats <- unique(fgsea_res$MvF_SED$gs_subcat)
for (subcat_i in subcats) {
db_i <- sub(":", "-", subcat_i)
for (name_i in names(res_list)) {
if (name_i == "trained_vs_SED") {
column_labels <- rep(paste0(2 ^ (0:3), "W - SED"), times = 2)
anno_df <- data.frame(Sex = rep(c("Female", "Male"), each = 4))
anno_colors <- list(Sex = c("Female" = "#ff6eff", "Male" = "#5555ff"))
top_annotation <- HeatmapAnnotation(
df = anno_df, col = anno_colors,
gap = unit(0, "points"),
gp = gpar(col = "black"),
annotation_name_gp = gpar(fontsize = 7 * scale),
annotation_legend_param = list(
nrow = 2, by_row = T,
grid_height = unit(14, "points"),
grid_width = unit(14, "points"),
border = "black",
title_gp = gpar(fontsize = 7 * scale),
labels_gp = gpar(fontsize = 6 * scale)
))
column_split <- anno_df$Sex
colors <- c("#3366ff", "darkred", "grey")
} else {
column_labels <- latex2exp::TeX(
sprintf("(%dW - SED)$_{M - F}$", 2^(0:3))
)
top_annotation <- column_split <- NULL
colors <- c("#ff6eff", "#5555ff", "grey")
}
x <- res_list[[name_i]]
if ("gs_description" %in% colnames(x)) {
x <- x %>%
filter(gs_subcat == subcat_i) %>%
mutate(
row_labels = cutstr(gs_description, split = " ", n = 35),
row_labels = ifelse(nchar(gs_description) > 35 + nchar(pathway) + 5,
sprintf("%s...(%s)", row_labels, pathway),
gs_description),
keep_ids = NULL)
} else {
x <- mutate(x, row_labels = pathway)
}
enrichmat(x,
n_top = 20,
colors = colors,
scale_by = "row",
cell_size = unit(6 * scale, "points"),
padj_legend_title = "BH-adjusted\np-value",
padj_args = list(
title_gp = gpar(fontsize = 7 * scale),
labels_gp = gpar(fontsize = 6 * scale),
grid_height = unit(6 * scale, "pt"),
grid_width = unit(6 * scale, "pt")
),
rownames_column = "row_labels",
heatmap_args = list(
column_labels = column_labels,
column_title_side = "bottom",
column_split = column_split,
column_title = NULL,
column_names_gp = gpar(fontsize = 5.5 * scale),
row_names_gp = gpar(fontsize = 5.5 * scale),
top_annotation = top_annotation,
heatmap_legend_param = list(
title_gp = gpar(fontsize = scale * 6.5),
labels_gp = gpar(fontsize = scale * 6),
direction = direction,
legend_width = legend_width,
legend_height = legend_height
)
),
filename = sprintf(file.path("..", "..", "plots", "FGSEA_%s_%s_%s.pdf"),
ome, name_i, db_i),
height = scale * unit(3, "in"),
width = scale * unit(3, "in"),
units = "in",
draw_args = list(
legend_gap = unit(0.4, "in"),
gap = unit(0.5, "in"),
adjust_annotation_extension = TRUE,
heatmap_legend_side = heatmap_legend_side,
annotation_legend_side = annotation_legend_side,
align_annotation_legend = align_annotation_legend,
align_heatmap_legend = align_heatmap_legend
),
save_args = list(dpi = 400, res = 400, family = "ArialMT")
)
}
}
}