plot_heatmap: Create a heatmap with tracks and dendrograms from any matrix.

In cmmr/rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

Create a heatmap with tracks and dendrograms from any matrix.

Description

Create a heatmap with tracks and dendrograms from any matrix.

Usage

plot_heatmap(
  mtx,
  grid = list(label = "Grid Value", colors = "imola"),
  tracks = NULL,
  label = TRUE,
  label_size = NULL,
  rescale = "none",
  trees = TRUE,
  clust = "complete",
  dist = "euclidean",
  asp = 1,
  tree_height = 10,
  track_height = 10,
  legend = "right",
  title = NULL,
  xlab.angle = "auto",
  ...
)

Arguments

mtx	A numeric matrix with named rows and columns.
grid	Color palette name, or a list with entries for label, colors, range, bins, na.color, and/or guide. See the Track Definitions section for details. Default: list(label = "Grid Value", colors = "imola")
tracks	List of track definitions. See details below. Default: NULL.
label	Label the matrix rows and columns. You can supply a list or logical vector of length two to control row labels and column labels separately, for example label = c(rows = TRUE, cols = FALSE), or simply label = c(T, F). Other valid options are "rows", "cols", "both", "bottom", "right", and "none". Default: TRUE
label_size	The font size to use for the row and column labels. You can supply a numeric vector of length two to control row label sizes and column label sizes separately, for example c(rows = 20, cols = 8), or simply c(20, 8). Default: NULL, which computes: pmax(8, pmin(20, 100 / dim(mtx)))
rescale	Rescale rows or columns to all have a common min/max. Options: "none", "rows", or "cols". Default: "none"
trees	Draw a dendrogram for rows (left) and columns (top). You can supply a list or logical vector of length two to control the row tree and column tree separately, for example trees = c(rows = T, cols = F), or simply trees = c(T, F). Other valid options are "rows", "cols", "both", "left", "top", and "none". Default: TRUE
clust	Clustering algorithm for reordering the rows and columns by similarity. You can supply a list or character vector of length two to control the row and column clustering separately, for example clust = c(rows = "complete", cols = NA), or simply clust = c("complete", NA). Options are: FALSE or NA - Disable reordering. An hclust class object E.g. from stats::hclust(). A method name - "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", or "centroid". Default: "complete"
dist	Distance algorithm to use when reordering the rows and columns by similarity. You can supply a list or character vector of length two to control the row and column clustering separately, for example dist = c(rows = "euclidean", cols = "maximum"), or simply dist = c("euclidean", "maximum"). Options are: A dist class object E.g. from stats::dist() or bdiv_distmat(). A method name - "euclidean", "maximum", "manhattan", "canberra", "binary", or "minkowski". Default: "euclidean"
asp	Aspect ratio (height/width) for entire grid. Default: 1 (square)
tree_height, track_height	The height of the dendrogram or annotation tracks as a percentage of the overall grid size. Use a numeric vector of length two to assign c(top, left) independently. Default: 10 (10% of the grid's height)
legend	Where to place the legend. Options are: "right" or "bottom". Default: "right"
title	Plot title. Default: NULL.
xlab.angle	Angle of the labels at the bottom of the plot. Options are "auto", '0', '30', and '90'. Default: "auto".
...	Additional arguments to pass on to ggplot2::theme().

Value

A ggplot2 plot.
The computed data points and ggplot command are available as ⁠$data⁠ and ⁠$code⁠, respectively.

Track Definitions

One or more colored tracks can be placed on the left and/or top of the heatmap grid to visualize associated metadata values.

## Categorical ----------------------------
cat_vals = sample(c("Male", "Female"), 10, replace = TRUE)
tracks   = list('Sex' = cat_vals)
tracks   = list('Sex' = list('values' = cat_vals, 'colors' = "bright"))
tracks   = list('Sex' = list(
  'values' = cat_vals, 
  'colors' = c('Male' = "blue", 'Female' = "red")) )

## Numeric --------------------------------
num_vals = sample(25:40, 10, replace = TRUE)
tracks   = list('Age' = num_vals)
tracks   = list('Age' = list('values' = num_vals, 'colors' = "greens"))
tracks   = list('Age' = list('values' = num_vals, 'range' = c(0,50)))
tracks   = list('Age' = list(
  'label'  = "Age (Years)",
  'values' = num_vals, 
  'colors' = c("azure", "darkblue", "darkorchid") ))

## Multiple Tracks ------------------------
tracks = list('Sex' = cat_vals, 'Age' = num_vals)
tracks = list(
  list('label' = "Sex", values' = cat_vals, 'colors' = "bright"),
  list('label' = "Age", values' = num_vals, 'colors' = "greens") )
  
plot_heatmap(matrix(sample(1:50), ncol=10), tracks = tracks)

The following entries in the track definitions are understood:

• values - The metadata values. When unnamed, order must match matrix.

• range - The c(min,max) to use for scale values.

• label - Label for this track. Defaults to the name of this list element.

• side - Options are "top" (default) or "left".

• colors - A pre-defined palette name or custom set of colors to map to.

• na.color - The color to use for NA values.

• bins - Bin a gradient into this many bins/steps.

• guide - A list of arguments for guide_colorbar() or guide_legend().

All built-in color palettes are colorblind-friendly. See Mapping Metadata to Aesthetics for images of the palettes.

Categorical palette names: "okabe", "carto", "r4", "polychrome", "tol", "bright", "light", "muted", "vibrant", "tableau", "classic", "alphabet", "tableau20", "kelly", and "fishy".

Numeric palette names: "reds", "oranges", "greens", "purples", "grays", "acton", "bamako", "batlow", "bilbao", "buda", "davos", "devon", "grayC", "hawaii", "imola", "lajolla", "lapaz", "nuuk", "oslo", "tokyo", "turku", "bam", "berlin", "broc", "cork", "lisbon", "roma", "tofino", "vanimo", and "vik".

See Also

Other visualization: adiv_boxplot(), adiv_corrplot(), bdiv_boxplot(), bdiv_corrplot(), bdiv_heatmap(), bdiv_ord_plot(), rare_corrplot(), rare_multiplot(), rare_stacked(), stats_boxplot(), stats_corrplot(), taxa_boxplot(), taxa_corrplot(), taxa_heatmap(), taxa_stacked()

Examples

    library(rbiom) 
    
    set.seed(123)
    mtx <- matrix(runif(5*8), nrow = 5, dimnames = list(LETTERS[1:5], letters[1:8]))
    
    plot_heatmap(mtx)
    plot_heatmap(mtx, grid="oranges")
    plot_heatmap(mtx, grid=list(colors = "oranges", label = "Some %", bins = 5))
    
    tracks <- list(
      'Number' = sample(1:ncol(mtx)),
      'Person' = list(
        values = factor(sample(c("Alice", "Bob"), ncol(mtx), TRUE)),
        colors = c('Alice' = "purple", 'Bob' = "darkcyan") ),
      'State' = list(
        side   = "left",
        values = sample(c("TX", "OR", "WA"), nrow(mtx), TRUE),
        colors = "bright" )
    )
    
    plot_heatmap(mtx, tracks=tracks)
    

cmmr/rbiom documentation built on April 28, 2024, 6:38 a.m.