plotAbundance: Plotting abundance data
In microbiome/miaViz: Microbiome Analysis Plotting and Visualization

plotAbundance

R Documentation

Plotting abundance data

Description

plotAbundance() creates a barplot of feature abundances, typically used to visualize the relative abundance of features at a specific taxonomy rank.

Usage

plotAbundance(x, ...)

## S4 method for signature 'SummarizedExperiment'
plotAbundance(
  x,
  assay.type = assay_name,
  assay_name = "counts",
  layout = "bar",
  ...
)

Arguments

`x`	a `SummarizedExperiment` object.
`...`	additional parameters for plotting. `group`: `Character scalar`. Specifies the group for agglomeration. Must be a value from `colnames(rowData(x))`. If `NULL`, agglomeration is not applied. (Default: `NULL`) `as.relative`: `Character scalar`. Should the relative values be calculated? (Default: `FALSE`) `col.var`: `Character scalar`. Selects a column from `colData` to be plotted below the abundance plot. Continuous numeric values will be plotted as point, whereas factors and character will be plotted as colour-code bar. (Default: `NULL`) `order.row.by`: `Character scalar`. How to order abundance value. By name (`"name"`) for sorting the taxonomic labels alphabetically, by abundance (`"abund"`) to sort by abundance values or by a reverse order of abundance values (`"revabund"`). (Default: `"name"`) `row.levels`: `Character vector`. Specifies order of rows in a plot. Can be combined with `order.row.by` to control order of only certain rows. If `NULL`, the order follows `order.row.by`. (Default: `NULL`) `order.col.by`: `Character scalar`. from the chosen rank of abundance data or from `colData` to select values to order the abundance plot by. (Default: `NULL`) `col.levels`: `Character vector`. Specifies order of columns in a plot. Can be combined with `order.col.by` to control order of only certain columns. If `NULL`, the order follows `order.col.by`. (Default: `NULL`) `decreasing`: `Logical scalar`. If the `order.col.by` is defined and the values are numeric, should the values used to order in decreasing or increasing fashion? (Default: `FALSE`) `facet.rows`: `Logical scalar`. Should the rows in the plot be spitted into facets? (Default: `FALSE`) `facet.cols`: `Logical scalar`. Should the columns in the plot be spitted into facets? (Default: `FALSE`) `ncol`: `Numeric scalar`. if facets are applied, `ncol` defines many columns should be for plotting the different facets. (Default: `2`) `scales` `Character scalar`. Defines the behavior of the scales of each facet. The value is passed into `facet_wrap`. (Default: `"fixed"`) See `mia-plot-args` for more details i.e. call `help("mia-plot-args")`
`assay.type`	`Character scalar` value defining which assay data to use. (Default: `"relabundance"`)
`assay_name`	Deprecate. Use `assay.type` instead.
`layout`	`Character scalar`. Either “bar” or “point”.

Details

It is recommended to handle subsetting, agglomeration, and transformation outside this function. However, agglomeration and relative transformation can be applied using the group and as.relative parameters, respectively. If one of the TAXONOMY_RANKS is selected via group, mia::agglomerateByRank() is used, otherwise agglomerateByVariable() is applied.

Value

a ggplot object or list of two ggplot objects, if col.var are added to the plot.

Examples

data(GlobalPatterns, package="mia")
tse <- GlobalPatterns

# If rank is set to NULL (default), agglomeration is not done. However, note
# that there is maximum number of rows that can be plotted. That is why
# we take sample from the data.
set.seed(26348)
sample <- sample(rownames(tse), 20)
tse_sub <- tse[sample, ]
# Apply relative transformation
tse_sub <- transformAssay(tse_sub, method = "relabundance")
plotAbundance(tse_sub, assay.type = "relabundance")

# Plotting counts using the first taxonomic rank as default
plotAbundance(
    tse, assay.type="counts", group = "Phylum") +
    labs(y="Counts")

# Using "Phylum" as rank. Apply relative transformation to "counts" assay.
plotAbundance(
    tse, assay.type="counts", group = "Phylum", add_legend = FALSE,
    as.relative = TRUE)

# Apply relative transform
tse <- transformAssay(tse, method = "relabundance")

# A feature from colData or taxon from chosen rank can be used for ordering
# samples.
plotAbundance(
    tse, assay.type="relabundance", group = "Phylum",
    order.col.by = "Bacteroidetes")

# col.var from colData can be plotted together with abundance plot.
# Returned object is a list that includes two plot; other visualizes
## abundance other col.var.
plot <- plotAbundance(
    tse, assay.type = "relabundance", group = "Phylum",
    col.var = "SampleType")

# These two plots can be combined with wrap_plots function from patchwork
# package
library(patchwork)
wrap_plots(plot, ncol = 1, heights = c(0.95, 0.05))


# Same plot as above but showing sample IDs as labels for the x axis on the
# top plot. Moreover, we use facets.
plot <- plotAbundance(
    tse, assay.type = "relabundance",
    group = "Phylum", col.var = "SampleType", add.legend = FALSE,
    add.x.text = TRUE, facet.cols = TRUE, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90))
plot

# Compositional barplot with top 5 taxa and samples sorted by
# "Bacteroidetes"

# Getting top taxa on a Phylum level
tse <- transformAssay(tse, method = "relabundance")
tse_phylum <- agglomerateByRank(tse, rank = "Phylum")
top_taxa <- getTop(tse_phylum, top = 5, assay.type = "relabundance")

# Renaming the "Phylum" rank to keep only top taxa and the rest to "Other"
phylum_renamed <- lapply(rowData(tse)$Phylum, function(x){
    if (x %in% top_taxa) {x} else {"Other"}})
rowData(tse)$Phylum <- as.character(phylum_renamed)

# Compositional barplot
plotAbundance(
    tse, assay.type="relabundance", group = "Phylum",
    order.row.by="abund", order.col.by = "Bacteroidetes")

microbiome/miaViz documentation built on April 13, 2025, 3:29 a.m.