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

Description Usage Arguments Details Value Author(s) See Also Examples

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,
    
    ...)

`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`.

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.

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

Zuguang Gu <z.gu@dkfz.de>

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

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"))

Loading required package: grid
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ComplexHeatmap version 2.6.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
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