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Plot Types

Source: vignettes/articles/plots.Rmd
plots.Rmd

Plotting functions in rbiom fall into five categories:

Category Functions
Box Plots adiv_boxplot() bdiv_boxplot() taxa_boxplot()
Correlation Plots adiv_corrplot() bdiv_corrplot() taxa_corrplot()
Ordination Plots bdiv_ord_plot()
Heatmaps bdiv_heatmap() taxa_heatmap() plot_heatmap()
Stacked Bar Plots taxa_stacked()

Box Plots

Box plots are useful for visualizing a numeric outcome against one or more categorical predictors. The rbiom package provides dedicated functions for three numeric outcomes:

  1. Alpha Diversity (shannon, simpson, etc) - adiv_boxplot().
  2. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_boxplot().
  3. Taxa Abundance (phylum, genus, etc) - taxa_boxplot().

You can then map categorical metadata variables to the x-axis, colors, patterns, shapes, and facets. See Mapping Metadata to Aesthetics for details.

Layers

Despite being refered to as “box plots”, rbiom’s *_boxplot() functions support a wide range of graphical elements beyond box-and-whisker.

You can assign one or more of the following options to a box plot’s layers parameter.

Unambiguous abbreviations are also accepted. For instance, layers = c("box", "dot") is equivalent to layers = c("x", "d") and layers = "xd". Note that the single letter abbreviation for “box” is “x” (“bar” is “b”).

Examples 

biom <- rarefy(hmp50, depth = 1000)

adiv <- adiv_boxplot(
  biom = biom, layers = "bes", x = "Sex", stat.by = "Sex")
  
bdiv <- bdiv_boxplot(
  biom = biom, layers = "xd", x = "==Body Site", stat.by = "Body Site", 
  pt.alpha = 0.2, pt.stroke = 0 )

taxa <- taxa_boxplot(
  biom = biom, layers = "p", x = "Body Site", p.label = 0, taxa = 3, 
  facet.ncol = 1, facet.strip.position = "right" )

patchwork::wrap_plots(
  lapply(list(adiv, bdiv, taxa), function (p) {
    p + 
      ggplot2::labs(x = NULL, caption = NULL) + 
      ggplot2::theme(legend.position = "none") }))
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_segment()`).

Statistics

The categorical groups defined by x, stat.by, etc are used to calculate statistics: Mann-Whitney or Kruskal-Wallis tests for alpha diversity and taxa abundance, and PERMANOVA (permutation tests on samples) for beta diversity. See Statistics for details.

Correlation Plots

Correlation plots are designed to visualize the relationship between a numeric outcome and a numeric predictor, optionally with additional categorical predictors. The rbiom package provides dedicated functions for three numeric outcomes:

  1. Alpha Diversity (shannon, simpson, etc) - adiv_corrplot().
  2. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_corrplot().
  3. Taxa Abundance (phylum, genus, etc) - taxa_corrplot().

You can then map categorical metadata variables to the x-axis, colors, and facets. See Mapping Metadata to Aesthetics for details.

Layers

You can assign one or more of the following options to a correlation plot’s layers parameter.

Unambiguous abbreviations are also accepted. For instance, layers = c("trend", "point") is equivalent to layers = c("t", "p") and layers = "tp".

Examples 

biom <- rarefy(hmp50, depth = 1000)

adiv <- adiv_corrplot(biom, layers = "ts", x = "BMI")
  
bdiv <- bdiv_corrplot(
  biom    = subset(biom, `Body Site` %in% c("Buccal mucosa", "Stool")),
  layers  = "tc", x = "BMI", stat.by = "==Body Site" )

taxa <- taxa_corrplot(
  biom    = subset(biom, `Body Site` %in% c("Buccal mucosa", "Saliva", "Stool")),
  layers  = "tsr", x = "Age", taxa = 3, stat.by = "Body Site",
  facet.ncol = 1, facet.strip.position = "right" )

patchwork::wrap_plots(
  guides = "collect",
  lapply(list(adiv, bdiv, taxa), function (p) {
    p + ggplot2::labs(caption = NULL) }))

Ordination Plots

Layers

You can assign one or more of the following options to a ordination plot’s layers parameter.

Unambiguous abbreviations are also accepted. For instance, layers = c("point", "ellipse") is equivalent to layers = c("p", "e") and layers = "pe".

The layers c("point", "spider", "ellipse", "name") apply to samples. The layers c("mean", "taxon", "arrow") apply to the taxa.

Examples 

biom <- rarefy(hmp50)

p1 <- bdiv_ord_plot(biom, layers = "pse", stat.by = "Body Site") +
        ggplot2::theme(legend.position = "none")

p2 <- bdiv_ord_plot(biom, layers = "emt", stat.by = "Body Site")

patchwork::wrap_plots(p1, p2, guides = "collect")

Heatmaps

Visualizing a large grid of values is a job for heatmaps. The generic plot_heatmap() function accepts any matrix, while the two common use cases below operate on a biom object.

  1. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_heatmap().
  2. Taxa Abundance (phylum, genus, etc) - taxa_heatmap().

Examples 

biom <- rarefy(hmp50)

bdiv <- bdiv_heatmap(biom, tracks = c("Age", "Body Site"), asp = 0.4)
taxa <- taxa_heatmap(biom, tracks = c("Age", "Body Site"), asp = 0.4)

patchwork::wrap_plots(bdiv, taxa, ncol = 1, guides = "collect")

Stacked Bar Plots

Taxa stacked bar plots show the same information as a taxa heatmap. Both have their own advantages.

Examples 

taxa_stacked(rarefy(hmp50), taxa = 10)

Publication Figures

Every rbiom plot uses theme_rbiom(): black sans-serif text, open axes with outward ticks, no gridlines or shading, and colorblind-friendly colors. Journals typically want 5-8 pt text at the final printed size. Set the text size with base_size (or p + theme(text = element_text(size = 8)) afterwards) and save the figure at its final width, for example 7 inches (180 mm) for a two-column figure.

p <- adiv_boxplot(biom, x = "Body Site", stat.by = "Body Site", base_size = 8)
ggsave("figure2.pdf", p, width = 7, height = 3.5)

Gridlines and a panel box are options of the theme, and theme_rbiom() gives your own ggplot2 figures the same style.

adiv_boxplot(biom, x = "Body Site", stat.by = "Body Site", grid = "y", frame = "box")


ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg)) +
  ggplot2::geom_point() +
  theme_rbiom()