Visualize Density Distribution by Heatmap

Visualize Density Distribution by Heatmap

densityHeatmap(data,
    density_param = list(na.rm = TRUE),

    col = rev(brewer.pal(11, "Spectral")),
    color_space = "LAB",
    ylab = deparse(substitute(data)),
    column_title = paste0("Density heatmap of ", deparse(substitute(data))),
    title = column_title,
    ylim = NULL,
    range = ylim,

    title_gp = gpar(fontsize = 14),
    ylab_gp = gpar(fontsize = 12),
    tick_label_gp = gpar(fontsize = 10),
    quantile_gp = gpar(fontsize = 10),
    show_quantiles = TRUE,

    column_order = NULL,
    column_names_side = "bottom",
    show_column_names = TRUE,
    column_names_max_height = unit(6, "cm"),
    column_names_gp = gpar(fontsize = 12),
    column_names_rot = 90,

    cluster_columns = FALSE,
    clustering_distance_columns = "ks",
    clustering_method_columns = "complete",
    mc.cores = 1,

    ...)

Arguments

data	A matrix or a list. If it is a matrix, density is calculated by columns.
density_param	Parameters send to density, na.rm is enforced to be TRUE.
col	A vector of colors that density values are mapped to.
color_space	The color space in which colors are interpolated. Pass to colorRamp2.
ylab	Label on y-axis.
column_title	Title of the heatmap.
title	Same as column_title.
ylim	Ranges on the y-axis.
range	Same as ylim.
title_gp	Graphic parameters for title.
ylab_gp	Graphic parameters for y-labels.
tick_label_gp	Graphic parameters for y-ticks.
quantile_gp	Graphic parameters for the quantiles.
show_quantiles	Whether show quantile lines.
column_order	Order of columns.
column_names_side	Pass to Heatmap.
show_column_names	Pass to Heatmap.
column_names_max_height	Pass to Heatmap.
column_names_gp	Pass to Heatmap.
column_names_rot	Pass to Heatmap.
cluster_columns	Whether cluster columns?
clustering_distance_columns	There is a specific distance method ks which is the Kolmogorov-Smirnov statistic between two distributions. For other methods, the distance is calculated on the density matrix.
clustering_method_columns	Pass to Heatmap.
mc.cores	Multiple cores for calculating ks distance.
...	Pass to Heatmap.

Details

To visualize data distribution in a matrix or in a list, we normally use boxplot or violinplot. We can also use colors to map the density values and visualize distribution of values through a heatmap. It is useful if you have huge number of columns in data to visualize.

The density matrix is generated with 500 rows ranging between the maximun and minimal values in all densities.

Value

A Heatmap-class object. It can oly add other heatmaps/annotations vertically.

See also

https://jokergoo.github.io/ComplexHeatmap-reference/book/other-high-level-plots.html#density-heatmap

Examples

matrix = matrix(rnorm(100), 10); colnames(matrix) = letters[1:10]
densityHeatmap(matrix)

lt = list(rnorm(10), rnorm(10))
densityHeatmap(lt)

ha = HeatmapAnnotation(points = anno_points(runif(10)),
    anno = rep(c("A", "B"), each = 5), col = list(anno = c("A" = "red", "B" = "blue")))
densityHeatmap(matrix, top_annotation = ha)
densityHeatmap(matrix, top_annotation = ha) %v% Heatmap(matrix, height = unit(6, "cm"))