Stacked Barplots

This vignette explains in more detail how to modify the stacked bar plots of tidytacos. We will illustrate those using a dataset with human microbiome samples from the upper respiratory tract (URT), taken from this paper by De Boeck et al. It contains nose as well as nasopharynx samples. Most samples were taken using a swab method, but a minority was taking with the aspirate method.

Setting up

We need only two packages: tidytacos (of course) and the tidyverse set of packages.

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidytacos)
#> 
#> Attaching package: 'tidytacos'
#> 
#> The following object is masked from 'package:dplyr':
#> 
#>     everything
#> 
#> The following object is masked from 'package:tidyr':
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#>     everything

Making a stacked barplot of a subset of samples

We start by selecting the samples we are interesting in: only the ones from the nose, taken with the swab method.

urt_ns <- urt %>%
  filter_samples(location == "N", method == "S")

We can very easily create explorative plots of our samples in the following ways:

tacoplot_stack(urt_ns)

tacoplot_stack_ly(urt_ns, x=sample)

The tacoplot_stack function returns a nice bar plot visualization of the most abundant taxa in our samples. The tacoplot_stack_ly function returns an interactive version of the same bar plot. The order of the samples on the x-axis is determined by hierarchical clustering of the visualized sample composition. In addition, these functions do some things behind the screens:

Add relative abundances to the counts table.
For each taxon, calculate the mean relative abundance across all samples where this taxon occurs.
Give all taxa a human-understandable name so that the taxon name is unique. This is just the genus name of the taxon, followed by a number to make it unique. Taxa with a higher mean relative abundance get a smaller number. E.g. “Lactobacillus 1” is the Lactobacillus taxon with the largest mean relative abundance.
Make a new variable that is equal to the taxon name, except that only the top-11 taxa retain their name and all others are changed to “Other taxa”. This is for visualization purposes; the human eye can only discriminate clearly between about 12 colors.

All these new variables are created under the hood, but are gone when the function execution is finished (there are no so-called “side-effects”). Luckily, for each of these variables there also exists a function that will create it and keep it! These functions are the following (there names are intended to be self-explanatory as much as possible):

add_rel_abundance: adds to counts table
add_mean_rel_abundance: adds to taxa table
add_taxon_name: adds to taxa table
add_taxon_name_color: adds to taxa table

There are more of such functions that add an extra variable to one of the tables; we will explore them further in other parts of this vignette.

There are two interesting types of facet wrapped bar plots we can make: facets with fixed x-axis categories, and facets without fixed x-axis categories.

Facets with fixed x-axis categories

The first type is a plot where the variable on the x-axis is not the sample name (the not-very-informative default). We want the categories of that variable in the same order in all our subplots. For example, we have a subplot for every participant, and for each participant we want to see the nose sample and the nasopharynx sample, in that order. This is achieved by adding a x = location extra argument to the barplot function, and adding a facet_wrap() layer. Putting the x-axis categories in the same order in all subplots is the default behaviour of the facet_wrap() function. Note that the order of the samples in each subplot is now determined by the categories of the variable we put there (e.g. alphabetically sorted), and no longer by a sample clustering procedure!

urt_s <- urt%>%
  filter_samples(method == "S")

tacoplot_stack(urt_s, x = location) +
  facet_wrap(~ participant, nrow = 10)
#> Warning in tacoplot_stack(urt_s, x = location): Sample labels not unique,
#> samples are aggregated.

Facets without fixed x-axis categories

The second type of facet wrapped plot is one where we have the sample names on the x-axis, as usual, and they are sorted according to a clustering procedure. For example, we want one facet per sampling location, with only the samples belonging to that location. This can be achieved in the following way:

tacoplot_stack(urt_s) +
  facet_wrap(~ location, nrow = 2)

This is not quite right. The default behavior of facet_wrap() is to repeat all possible x-axis values in all facets, even if there is no information there! In our case, the default behaviour would be to put all samples names in the nose facet and nasopharynx facet, and plot empty space if a sample name - sampling location combination doesn’t exist. Adding the argument scales = "free_x" corrects this behavior and also makes sure that the samples are plotted in order of clustering:

tacoplot_stack(urt_s) +
  facet_wrap(~ location, scales = "free_x", nrow = 2)

Plotting only a subset of taxa, without “Other taxa”

To be able to do this, we need to do a number of things by hand that normally happen “under the hood” in the tacoplot_stack() function:

Step 1: We add relative abundances. We want them to be calculated with respect to the full samples, before the taxa we’re not interested in are removed!
Step 2: We select only the taxa we want (e.g. family Lactobacillaceae).
Step 3: We add the variable “taxon_name_color”; this variable is equal to “taxon_name”, except that everything apart from the n most abundant taxa (which are at this moment all Lactobacillaceae!) will be called “Other taxa”.
Step 4: We make the plot.

Thanks to the %>% (pipe) operator from the magrittr package, we can achieve all this using the following elegant code:

urt %>%
  add_rel_abundance() %>%
  filter_taxa(family == "Lactobacillaceae") %>%
  add_taxon_name_color() %>%
  tacoplot_stack()+
    geom_point(aes(y=-0.01,color=location,shape=method))

Setting up

Making a stacked barplot of a subset of samples

Making a facet wrapped stacked barplot

Plotting only a subset of taxa, without “Other taxa”