heart_revremod_paper/analysis/bulk/bulk_05_clusters_funcomics_visualisation.R at main · saezlab/heart_revremod_paper · GitHub

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# Copyright (c) [2025] [Ines Rivero Garcia]
# ines.rivero@uni-heidelberg.de

# Visualise mRNA-seq funcomics results

library(dplyr)
library(ggplot2)
library(ComplexHeatmap)
setwd("/mnt/sds-hd/sd22b002/projects/ines/heart_revremod/")

# Visualise hallmarks
hallmarks <- read.csv("results/bulk/mrna_time_clusters_funcomics_hallmarks.csv", header = TRUE, row.names = 1)
top_hallmarks <- hallmarks %>%
    filter(fdr < 0.05) %>%
    filter(activity > 0) %>%
    arrange(fdr, activity) %>%
    pull(gene) %>%
    unique()

hallmarks_mat <- hallmarks %>%
    filter(gene %in% top_hallmarks) %>%
    mutate(gene_cluster = paste0("cluster_", gene_cluster)) %>%
    select(gene_cluster, gene, activity) %>%
    tidyr::pivot_wider(names_from = gene_cluster, values_from = activity) %>%
    tibble::column_to_rownames("gene") %>%
    as.matrix()

hallmarks_fdr <- hallmarks %>%
    filter(gene %in% top_hallmarks) %>%
    mutate(gene_cluster = paste0("cluster_", gene_cluster)) %>%
    select(gene_cluster, gene, fdr) %>%
    tidyr::pivot_wider(names_from = gene_cluster, values_from = fdr) %>%
    tibble::column_to_rownames("gene") %>%
    as.matrix()
hallmarks_fdr <- ifelse(hallmarks_fdr < 0.05, "*", "")


pdf("results/bulk/mrna_time_clusters_hallmarks_heatmap.pdf", height = 6, width = 4)
Heatmap(hallmarks_mat,
  name = "pathway\nactivity",
  #col = col_fun,
  cell_fun = function(j, i, x, y, width, height, fill) {
    if (hallmarks_fdr[i, j] == "*") {
      grid.text("*", x, y, gp = gpar(fontsize = 14, fontface = "bold"))
    }
  },
  cluster_rows = TRUE,
  cluster_columns = TRUE
)
dev.off()

# Visualise TF activities
collectri <- read.csv("results/bulk/mrna_time_clusters_funcomics_collectri.csv", header = TRUE, row.names = 1)
top_collectri <- collectri %>%
    filter(fdr < 0.05) %>%
    filter(activity > 0) %>%
    arrange(fdr, activity) %>%
    pull(gene) %>%
    unique() %>%
    head(n=30)

collectri_mat <- collectri %>%
    filter(gene %in% top_collectri) %>%
    mutate(gene_cluster = paste0("cluster_", gene_cluster)) %>%
    select(gene_cluster, gene, activity) %>%
    tidyr::pivot_wider(names_from = gene_cluster, values_from = activity) %>%
    tibble::column_to_rownames("gene") %>%
    as.matrix()

collectri_fdr <- collectri %>%
    filter(gene %in% top_collectri) %>%
    mutate(gene_cluster = paste0("cluster_", gene_cluster)) %>%
    select(gene_cluster, gene, fdr) %>%
    tidyr::pivot_wider(names_from = gene_cluster, values_from = fdr) %>%
    tibble::column_to_rownames("gene") %>%
    as.matrix()
collectri_fdr <- ifelse(collectri_fdr < 0.05, "*", "")

# color palette to nicely show activities
col_fun = circlize::colorRamp2(breaks = seq(min(collectri_mat), max(collectri_mat), length.out = 9),
                     colors = c("#fff5eb", "#fee6ce", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#a63603", "#7f2704"))

pdf("results/bulk/mrna_time_clusters_collectri_heatmap.pdf", height = 6, width = 4)
Heatmap(collectri_mat,
  name = "pathway\nactivity",
  col = col_fun,
  cell_fun = function(j, i, x, y, width, height, fill) {
    if (collectri_fdr[i, j] == "*") {
      grid.text("*", x, y, gp = gpar(fontsize = 14, fontface = "bold"))
    }
  },
  cluster_rows = TRUE,
  cluster_columns = TRUE
)
dev.off()