densityHeatmap {ComplexHeatmap} | 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,

        ...)

### 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.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### 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"))

[Package ComplexHeatmap version 2.3.4 Index]