Contents

miaViz implements plotting function to work with TreeSummarizedExperiment and related objects in a context of microbiome analysis. For more general plotting function on SummarizedExperiment objects the scater package offers several options, such as plotColData, plotExpression and plotRowData.

1 Installation

To install miaViz, install BiocManager first, if it is not installed. Afterwards use the install function from BiocManager and load miaViz.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("miaViz")
library(miaViz)
data(GlobalPatterns, package = "mia")

2 Abundance plotting

in contrast to other fields of sequencing based fields of research for which expression of genes is usually studied, microbiome research uses the more term Abundance to described the numeric data measured and analyzed. Technically, especially in context of SummarizedExperiment objects, there is no difference. Therefore plotExpression can be used to plot Abundance data.

plotAbundance can be used as well and as long as rank is set NULL, it behaves as plotExpression.

plotAbundance(GlobalPatterns, rank = NULL, 
              features = "549322", assay_name = "counts")

However, if the rank is set not NULL a bar plot is returned. At the same time the features argument can be set to NULL (default).

GlobalPatterns <- transformCounts(GlobalPatterns, method = "relabundance")
plotAbundance(GlobalPatterns, rank = "Kingdom", assay_name = "relabundance")

With subsetting to selected features the plot can be fine tuned.

prev_phylum <- getPrevalentTaxa(GlobalPatterns, rank = "Phylum",
                                detection = 0.01)
plotAbundance(GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
              rank = "Phylum",
              assay_name = "relabundance")

The features argument is reused for plotting data along the different samples. In the next example the SampleType is plotted along the samples. In this case the result is a list, which can combined using external tools, for example patchwork.

library(patchwork)
plots <- plotAbundance(GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
                       features = "SampleType",
                       rank = "Phylum",
                       assay_name = "relabundance")
plots$abundance / plots$SampleType +
     plot_layout(heights = c(9, 1))

Further example about composition barplot can be found at Orchestrating Microbiome Analysis (Lahti, Shetty, and Ernst 2021).

3 Prevalence plotting

To visualize prevalence within the dataset, two functions are available, plotTaxaPrevalence, plotPrevalenceAbundance and plotPrevalence.

plotTaxaPrevalence produces a so-called landscape plot, which visualizes the prevalence of samples across abundance thresholds.

plotTaxaPrevalence(GlobalPatterns, rank = "Phylum",
                   detections = c(0, 0.001, 0.01, 0.1, 0.2))