Plot a complex heatmap

ComplexHeatmap.R

library(ComplexHeatmap)

count_matrix # if your data is called count_matrix

# Read data
se <- readRDS("data/se.rds")
count_matrix <- assay(se)

# Data transformation
count_matrix_log2 <- log2(count_matrix + 1)

count_matrix_scale <- 
  count_matrix_log2 %>% t() %>% scale(center = TRUE, scale = TRUE) %>% t() # This scaling step is easy for an error. Be careful, you are scaling "rows" or "columns".

# Define annotation
sample_site_color <- 
  colData(se) %>% as_tibble() %>% pull(biopsy_area) %>% unique()
color <- RColorBrewer::brewer.pal(length(sample_site_color), "Pastel2")
names(color) <- sample_site_color

gene_theme_color <- RColorBrewer::brewer.pal(length(top100_g$theme), "Accent")
names(gene_theme_color) <- top100_g$theme %>% unique()

heatmap_annotation <- 
  HeatmapAnnotation(
    `Tissue type` = colData(se) %>% as_tibble() %>% pull(skin_type), 
    `Anatomic region` = colData(se) %>% as_tibble() %>% pull(biopsy_area),
    col = list(`Tissue type` = 
                 c("LS" = "#eb2d0c", "NL" = "#eb8b9b", "HC" = "#91cf60"),
               `Anatomic region` = color),
    annotation_legend_param = 
      list(`Tissue type` = list(nrow = 1, title_gp = gpar(fontsize = 12),
                                labels_gp = gpar(fontsize = 10)),
           `Anatomic region` = list(nrow = 1, title_gp = gpar(fontsize = 12),
                                    labels_gp = gpar(fontsize = 10))
                                   ))
# Main body of heatmap
heatmap_top100 <- ComplexHeatmap::Heatmap(count_matrix,
                        name = "gene expression",
                        column_dend_reorder = TRUE,
                        # clustering_method_columns = "complete",
                        show_column_names = FALSE,
                        show_row_names = TRUE,
                        show_row_dend = FALSE,
                        row_names_gp = gpar(fontsize = 9),
                        top_annotation = heatmap_annotation,
                        heatmap_legend_param = list(
                          title_gp = gpar(fontsize = 12),
                          labels_gp = gpar(fontsize = 10)
                          ),
                        column_split = se_heatmap_top100$skin_type,
                        row_split = top100_g$theme,
                        row_title = gt_render(
                          c("Noncoding<br>RNA",
                            "Skin<br>barrier<br>and<br>physiology",
                            "Immune<br>response",
                            "G protein<br>coupled<br>receptor")
                        ),
                        row_title_rot = 0, 
                        row_title_side = "right",
                        row_title_gp = gpar(fill = gene_theme_color))

draw(heatmap_top100, heatmap_legend_side = "right",
     annotation_legend_side = "top", merge_legend = FALSE)
     
## Save your heatmap
png("data/supplementary/figure_1b.png", width = 480*6.5, height = 480*6, res=300)
draw(heatmap_top100, heatmap_legend_side = "right",
     annotation_legend_side = "top", merge_legend = FALSE)
dev.off()