bdiv_heatmap: Display beta diversities in an all vs all grid.

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

Display beta diversities in an all vs all grid.

Description

Display beta diversities in an all vs all grid.

Usage

bdiv_heatmap(
  biom,
  bdiv = "Bray-Curtis",
  weighted = TRUE,
  tree = NULL,
  tracks = NULL,
  grid = "devon",
  label = TRUE,
  label_size = NULL,
  rescale = "none",
  clust = "complete",
  trees = TRUE,
  asp = 1,
  tree_height = 10,
  track_height = 10,
  legend = "right",
  title = TRUE,
  xlab.angle = "auto",
  underscores = FALSE,
  ...
)

Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
bdiv	Beta diversity distance algorithm(s) to use. Options are: "Bray-Curtis", "Manhattan", "Euclidean", "Jaccard", and "UniFrac". For "UniFrac", a phylogenetic tree must be present in biom or explicitly provided via ⁠tree=⁠. Multiple/abbreviated values allowed. Default: "Bray-Curtis"
weighted	Take relative abundances into account. When weighted=FALSE, only presence/absence is considered. Multiple values allowed. Default: TRUE
tree	A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree
tracks	A character vector of metadata fields to display as tracks at the top of the plot. Or, a list as expected by the tracks argument of plot_heatmap(). Default: NULL
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: "devon"
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(TRUE, FALSE). 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"
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"
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 = TRUE, cols = FALSE), or simply trees = c(TRUE, FALSE). Other valid options are "rows", "cols", "both", "left", "top", and "none". Default: TRUE
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. Set to TRUE for a default title, NULL for no title, or any character string. Default: TRUE
xlab.angle	Angle of the labels at the bottom of the plot. Options are "auto", '0', '30', and '90'. Default: "auto".
underscores	When parsing the tree, should underscores be kept as is? By default they will be converted to spaces (unless the entire ID is quoted). Default FALSE
...	Additional arguments to pass on to ggplot2::theme(). For example, labs.subtitle = "Plot subtitle".

Value

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

Annotation Tracks

Metadata can be displayed as colored tracks above the heatmap. Common use cases are provided below, with more thorough documentation available at https://cmmr.github.io/rbiom .

## Categorical ----------------------------
tracks = "Body Site"
tracks = list('Body Site' = "bright")
tracks = list('Body Site' = c('Stool' = "blue", 'Saliva' = "green"))

## Numeric --------------------------------
tracks = "Age"
tracks = list('Age' = "reds")

## Multiple Tracks ------------------------
tracks = c("Body Site", "Age")
tracks = list('Body Site' = "bright", 'Age' = "reds")
tracks = list(
  'Body Site' = c('Stool' = "blue", 'Saliva' = "green"),
  'Age'       = list('colors' = "reds") )

The following entries in the track definitions are understood:

colors -

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

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".

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.

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 beta_diversity: bdiv_boxplot(), bdiv_clusters(), bdiv_corrplot(), bdiv_ord_plot(), bdiv_ord_table(), bdiv_stats(), bdiv_table(), distmat_stats()

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

Examples

    library(rbiom)
    
    # Keep and rarefy the 10 most deeply sequenced samples.
    hmp10 <- rarefy(hmp50, n = 10)
    
    bdiv_heatmap(hmp10, tracks=c("Body Site", "Age"))
    
    bdiv_heatmap(hmp10, bdiv="uni", weighted=c(TRUE,FALSE), tracks="sex")

rbiom documentation built on April 3, 2025, 6:39 p.m.