Skip to contents

Display taxa abundances as a heatmap.

Source: R/taxa_heatmap.r
taxa_heatmap.Rd

Display taxa abundances as a heatmap.

Usage

taxa_heatmap(
  biom,
  rank = -1,
  taxa = 6,
  tracks = NULL,
  grid = "bilbao",
  other = FALSE,
  unc = "singly",
  lineage = FALSE,
  label = TRUE,
  label_size = NULL,
  rescale = "none",
  trees = TRUE,
  clust = "complete",
  dist = "euclidean",
  asp = 1,
  tree_height = 10,
  track_height = 10,
  legend = "right",
  title = TRUE,
  xlab.angle = "auto",
  ...
)

Arguments

biom

An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.

rank

What rank(s) of taxa to display. E.g. "Phylum", "Genus", ".otu", etc. An integer vector can also be given, where 1 is the highest rank, 2 is the second highest, -1 is the lowest rank, -2 is the second lowest, and 0 is the OTU "rank". Run biom$ranks to see all options for a given rbiom object. Default: -1.

taxa

Which taxa to display. An integer value will show the top n most abundant taxa. A value 0 <= n < 1 will show any taxa with that mean abundance or greater (e.g. 0.1 implies >= 10%). A character vector of taxa names will show only those named taxa. Default: 6.

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 as expected plot_heatmap(). Default: "bilbao"

other

Sum all non-itemized taxa into an "Other" taxa. When FALSE, only returns taxa matched by the taxa argument. Specifying TRUE adds "Other" to the returned set. A string can also be given to imply TRUE, but with that value as the name to use instead of "Other". Default: FALSE

unc

How to handle unclassified, uncultured, and similarly ambiguous taxa names. Options are:

"singly" -

Replaces them with the OTU name.

"grouped" -

Replaces them with a higher rank's name.

"drop" -

Excludes them from the result.

"asis" -

To not check/modify any taxa names.

Abbreviations are allowed. Default: "singly"

lineage

Include all ranks in the name of the taxa. For instance, setting to TRUE will produce Bacteria; Actinobacteria; Coriobacteriia; Coriobacteriales. Otherwise the taxa name will simply be Coriobacteriales. You want to set this to TRUE when unc = "asis" and you have taxa names (such as Incertae_Sedis) that map to multiple higher level ranks. Default: FALSE

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"

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

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. 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".

...

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.

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 taxa_abundance: sample_sums(), taxa_boxplot(), taxa_clusters(), taxa_corrplot(), taxa_stacked(), taxa_stats(), taxa_sums(), taxa_table()

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

Examples

    library(rbiom)
    
    # Keep and rarefy the 10 most deeply sequenced samples.
    hmp10 <- rarefy(hmp50, n = 10)
    
    taxa_heatmap(hmp10, rank = "Phylum", tracks = "Body Site")

    
    taxa_heatmap(hmp10, rank = "Genus", tracks = c("sex", "bo"))

    
    taxa_heatmap(hmp10, rank = "Phylum", tracks = list(
      'Sex'       = list(colors = c(m = "#0000FF", f = "violetred")), 
      'Body Site' = list(colors = "muted", label = "Source") ))