densityHeatmap: Visualize Density Distribution by Heatmap
In jokergoo/ComplexHeatmap: Make Complex Heatmaps

densityHeatmap

R Documentation

Visualize Density Distribution by Heatmap

Description

Visualize Density Distribution by Heatmap

Usage

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, cores = mc.cores,
    
    ...)

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. This argument will be removed in future versions.
`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.

Author(s)

Zuguang Gu <z.gu@dkfz.de>

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"))

jokergoo/ComplexHeatmap documentation built on Dec. 22, 2024, 8:51 a.m.