Plots
Colorblind-friendly palette
# The palette with grey:
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# The palette with black:
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# To use for fills, add
scale_fill_manual(values=cbPalette)
# To use for line and point colors, add
scale_colour_manual(values=cbPalette)
Main article: http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
Saving figures as SVG
library(svglite)
svglite(filename = paste0(plot_file,".svg"), width = 6, height = 12)
draw(hmap, padding = unit(c(1, 1, 1, 1), "cm")) # Bottom, left, top, right
dev.off()
Complex Heatmap
Complex heatmap complete reference book
Generate a data matrix for the heatmap
An example of generating a data matrix for heatmaps from a tibble. The code below generates two matrices, one for the estimates from a correlation test and the other for the p-values. Both matrices follow the same row and column name order. While creating the estimate matrix, I also generate a vector for row breaks. The Heatmap function shows how the two matrices and row break vector are used to create a heatmap using the estimates matrix for cell values, the p-values matrix to print * when significant, and the row breaks vector to horizontally group the cells.
est_mat <- dat %>% select(phylum,kingdom, cell_population, estimate) %>%
spread(cell_population, estimate) %>%
na.omit() %>%
as.data.frame() %>%
column_to_rownames(var = "phylum")
r_breaks <- est_mat$kingdom
est_mat <- est_mat %>% select(-kingdom) %>%
as.matrix()
p_mat <- dat %>% select(phylum, cell_population, p.value) %>%
spread(cell_population, p.value) %>%
na.omit() %>%
as.data.frame() %>%
column_to_rownames(var = "phylum") %>%
as.matrix()
hmap <- ComplexHeatmap::Heatmap(est_mat, cluster_rows = TRUE, cluster_columns = FALSE,
heatmap_legend_param = list(title = "Rho"),
row_split = r_breaks, # split rows
row_title_rot = 0, # rotate row title to horizontal, default is vertical
cell_fun = function(j, i, x, y, width, height, fill) {
if(p_mat[i, j] < 0.001){
grid.text("**", x, y, gp = gpar(fontsize = 6))
}else if(p_mat[i,j] > 0.001 & p_mat[i, j] < 0.05){
grid.text("*", x, y, gp = gpar(fontsize = 6))
}
},
column_names_gp = gpar(fontsize = 10),
row_names_gp = gpar(fontsize = 10))
Put text in the cells
hmap <- ComplexHeatmap::Heatmap(mat, cluster_rows = TRUE, cluster_columns = TRUE,
heatmap_legend_param = list(title = "Mean\nImportance"),
column_split = c_breaks, # split columns
row_split = r_breaks, # split rows
row_title_rot = 0, # rotate row title to horizontal, default is vertical
cell_fun = function(j, i, x, y, width, height, fill) {
if(mat_decision[i, j] == "Confirmed"){
grid.text("C", x, y, gp = gpar(fontsize = 6))
}else if(mat_decision[i, j] == "Tentative"){
grid.text("T", x, y, gp = gpar(fontsize = 6))
}
},
column_names_gp = gpar(fontsize = 10),
row_names_gp = gpar(fontsize = 10)
)
# Save the plot
plot_file <- file.path("figures", "heatpmap.png")
png(filename = plot_file, width = 12 * 300, height = 14 * 300, res = 300)
draw(hmap, padding = unit(c(3.5, 1, 1, 1), "cm")) # Bottom, left, top, right
dev.off()
Violin plot
A violin plot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data. It is used to visualize the distribution of numerical data. Unlike a box plot that can only show summary statistics, violin plots depict summary statistics and the density of each variable.