# miaViz

`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`.

# 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)
    library(scater)
    data(GlobalPatterns, package = "mia")

# 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 of a particular feature.

    plotExpression(GlobalPatterns, features = "549322", assay.type = "counts")

On the other hand, plotAbundance can be used to plot abundance by `rank`. A bar plot is returned showing the relative abundance within each sample for a given `rank`. At the same time the `features` argument can be set to `NULL` (default).

    GlobalPatterns <- transformAssay(GlobalPatterns, method = "relabundance")

    plotAbundance(GlobalPatterns, rank = "Kingdom", assay.type = "relabundance")

If `rank` is set to null however then the bars will be colored by each individual taxon. Please note that if you’re doing this make sure to agglomerate your data to a certain taxonomic hand before plotting.

    GlobalPatterns_king <- agglomerateByRank(GlobalPatterns, "Kingdom")
    plotAbundance(GlobalPatterns_king, assay.type = "relabundance")

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

    prev_phylum <- getPrevalent(GlobalPatterns, rank = "Phylum", detection = 0.01)

    plotAbundance(
        GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
        rank = "Phylum",
        assay.type = "relabundance")
    #> Warning: Removed 101 rows containing missing values or values outside the scale range
    #> (`geom_bar()`).

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)$Phylum %in% prev_phylum],
        features = "SampleType",
        rank = "Phylum",
        assay.type = "relabundance")
    plots$abundance / plots$SampleType + plot_layout(heights = c(9, 1))
    #> Warning: Removed 101 rows containing missing values or values outside the scale range
    #> (`geom_bar()`).

Further example about composition barplot can be found at Orchestrating Microbiome Analysis (Lahti et al. 2021).

# Prevalence plotting

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

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

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

`plotPrevalenceAbundance` plot the prevalence depending on the mean relative abundance on the chosen taxonomic level.

    plotPrevalentAbundance(GlobalPatterns, rank = "Family", colour.by = "Phylum") +
        scale_x_log10()
    #> Duplicated labels were made unique.

`plotPrevalence` plot the number of samples and their prevalence across different abundance thresholds. Abundance steps can be adjusted using the `detections` argument, whereas the analyzed prevalence steps is set using the `prevalences` argument.

    plotPrevalence(
        GlobalPatterns, rank = "Phylum",
        detections = c(0.01, 0.1, 1, 2, 5, 10, 20)/100,
        prevalences = seq(0.1, 1, 0.1))

# Tree plotting

The information stored in the `rowTree` can be directly plotted. However, sizes of stored trees have to be kept in mind and plotting of large trees rarely makes sense.

For this example we limit the information plotted to the top 100 taxa as judged by mean abundance on the genus level.

    library(scater)
    library(mia)

    altExp(GlobalPatterns,"Genus") <- agglomerateByRank(GlobalPatterns,"Genus")
    #> Duplicated labels were made unique.
    altExp(GlobalPatterns,"Genus") <- addPerFeatureQC(
        altExp(GlobalPatterns,"Genus"))
    rowData(altExp(GlobalPatterns,"Genus"))$log_mean <- log(
        rowData(altExp(GlobalPatterns,"Genus"))$mean)
    rowData(altExp(GlobalPatterns,"Genus"))$detected <- rowData(
        altExp(GlobalPatterns,"Genus"))$detected / 100
    top_taxa <- getTop(
        altExp(GlobalPatterns,"Genus"),
        method="mean",
        top=100L,
        assay.type="counts")

Colour, size and shape of tree tips and nodes can be decorated based on data present in the `SE` object or by providing additional information via the `other_fields` argument. Note that currently information for nodes have to be provided via the `other_fields` arguments.

Data will be matched via the `node` or `label` argument depending on which was provided. `label` takes precedent.

    plotRowTree(
        altExp(GlobalPatterns,"Genus")[top_taxa,], tip.colour.by = "log_mean",
        tip.size.by = "detected")

Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size)

Tip and node labels can be shown as well. Setting `show_label = TRUE` shows the tip labels only …

    plotRowTree(
        altExp(GlobalPatterns,"Genus")[top_taxa,],
        tip.colour.by = "log_mean", tip.size.by = "detected", show.label = TRUE)
    #> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
    #> ℹ Please use tidy evaluation idioms with `aes()`.
    #> ℹ See also `vignette("ggplot2-in-packages")` for more information.
    #> ℹ The deprecated feature was likely used in the miaViz package.
    #>   Please report the issue at <https://github.com/microbiome/miaViz/issues>.
    #> This warning is displayed once per session.
    #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    #> generated.

Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size). Tip labels of the tree are shown as well.

… whereas node labels can be selectively shown by providing a named logical vector to `show_label`.

Please note that currently `ggtree` can only plot node labels in a rectangular layout.

    labels <- c("Genus:Providencia", "Genus:Morganella", "0.961.60")
    plotRowTree(
        altExp(GlobalPatterns,"Genus")[top_taxa,],
        tip.colour.by = "log_mean",
        tip.size.by = "detected",
        show.label = labels,
        layout="rectangular")

Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size). Selected node and tip labels are shown.

Information can also be visualized on the edges of the tree plot.

    plotRowTree(
        altExp(GlobalPatterns,"Genus")[top_taxa,],
        edge.colour.by = "Phylum",
        tip.colour.by = "log_mean")

Tree plot using ggtree with tip labels decorated by mean abundance (colour) and edges labeled Kingdom (colour) and prevalence (size)

# Graph plotting

Similar to tree data, graph data can also be plotted in conjunction with `SummarizedExperiment` objects. Since the graph data in itself cannot be stored in a specialized slot, a graph object can be provided separately or as an element from the `metedata`.

Here we load an example graph. As graph data, all objects types accepted by `as_tbl_graph` from the `tidygraph` package are supported.

    data(col_graph)

In the following examples, the `weight` data is automatically generated from the graph data. The `SummarizedExperiment` provided is required to have overlapping rownames with the node names of the graph. Using this link the graph plot can incorporate data from the `SummarizedExperiment`.

    plotColGraph(
        col_graph,
        altExp(GlobalPatterns,"Genus"),
        colour.by = "SampleType",
        edge.colour.by = "weight",
        edge.width.by = "weight",
        show.label = TRUE)
    #> This graph was created by an old(er) igraph version.
    #> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version.
    #> For now we convert it on the fly...

As mentioned the graph data can be provided from the `metadata` of the `SummarizedExperiment`.

    metadata(altExp(GlobalPatterns,"Genus"))$graph <- col_graph

This produces the same plot as shown above.

# Plotting of serial data

    # Load data from miaTime package
    library("miaTime")
    data(SilvermanAGutData, package="miaTime")
    tse <- SilvermanAGutData
    tse <- transformAssay(tse, method = "relabundance")
    taxa <- getTop(tse, 2)

Data from samples collected along time can be visualized using `plotSeries`. The `time.col` argument is used to reference data from the `colData` to use as descriptor for ordering the data. The `features` argument selects the feature to show. Since plotting a lot of features is not advised a maximum of 20 features can plotted at the same time.

    plotSeries(
        tse,
        assay.type = "relabundance",
        time.col = "DAY_ORDER",
        features = taxa,
        colour.by = "Family",
        facet.by = "Vessel"
        )

If replicated data is present, data is automatically used for calculation of the `mean` and `sd` and plotted as a range. Data from different assays can be used for plotting via the `assay.type`.

    plotSeries(
        tse[taxa,],
        time.col = "DAY_ORDER",
        colour.by = "Family",
        linetype.by = "Phylum",
        assay.type = "relabundance")

Additional variables can be used to modify line type aesthetics.

    plotSeries(
        tse,
        time.col = "DAY_ORDER",
        features = getTop(tse, 5),
        colour.by = "Family",
        linetype.by = "Phylum",
        assay.type = "counts")

# Plotting factor data

To visualize the relative relations between two groupings among the factor data, two functions are available for the purpose; `plotColTile` and `plotRowTile`.

    data(GlobalPatterns, package="mia")
    se <- GlobalPatterns
    plotColTile(se,"SampleType","Primer") +
        theme(axis.text.x.top = element_text(angle = 45, hjust = 0))

# DMN fit plotting

Searching for groups that are similar to each other among the samples, could be approached with the Dirichlet Multinomial Mixtures (Holmes et al. 2012). After using `runDMN` from the `mia` package, several k values as a number of clusters are used to observe the best fit (see also `getDMN` and `getBestDMNFit`). To visualize the fit using e.g. “laplace” as a measure of goodness of fit:

    data(dmn_se, package = "mia")
    names(metadata(dmn_se))
    #> [1] "DMN"
    # plot the fit
    plotDMNFit(dmn_se, type = "laplace")
    #> Warning in .local(x, name, type, ...): 'getDMN' is deprecated.
    #> Use 'addCluster' instead.
    #> See help("Deprecated") and help("Now runDMN and calculateDMN are deprecated. Use addCluster with DMMParam parameter and full parameter set as true instead.-deprecated").

# Serial data ordination and trajectories

Principal Coordinates Analysis using Bray-Curtis dissimilarity on the `hitchip1006` dataset:

    library(miaTime)
    data(hitchip1006, package = "miaTime")
    tse <- hitchip1006
    tse <- transformAssay(tse, method = "relabundance")
    ## Ordination with PCoA with Bray-Curtis dissimilarity
    tse <- runMDS(
        tse, FUN = getDissimilarity, method = "bray", name = "PCoA_BC",
        assay.type = "relabundance", na.rm = TRUE)
    # plot
    p <- plotReducedDim(tse, dimred = "PCoA_BC")
    p

Retrieving information about all available trajectories:

    library(dplyr)

    # List subjects with two time points
    selected.subjects <- names(which(table(tse$subject)==2))

    # Subjects counts per number of time points available in the data
    table(table(tse$subject)) %>% as.data.frame() %>%
        rename(Timepoints=Var1, Subjects=Freq)

Lets look at all trajectories having two time points in the data:

    # plot
    p + geom_path(
        aes(x=X1, y=X2, group=subject),
        arrow=arrow(length = unit(0.1, "inches")),
        # combining ordination data and metadata then selecting the subjects
        # Note, scuttle::makePerCellDF could also be used for the purpose.
        data = subset(
            data.frame(reducedDim(tse), colData(tse)),
            subject %in% selected.subjects) %>% arrange(time)) +
        labs(title = "All trajectories with two time points") +
        theme(plot.title = element_text(hjust = 0.5))

Filtering the two time point trajectories by divergence and displaying top 10%:

    library(miaTime)
    # calculating step wise divergence based on the microbial profiles
    tse <- getStepwiseDivergence(tse, group = "subject", time_field = "time")
    # retrieving the top 10% divergent subjects having two time points
    top.selected.subjects <- subset(
        data.frame(reducedDim(tse), colData(tse)),
        subject %in% selected.subjects) %>%
        top_frac(0.1, time_divergence) %>% select(subject) %>% .[[1]]
    # plot
    p + geom_path(
        aes(x=X1, y=X2, color=time_divergence, group=subject),
        # the data is sorted in descending order in terms of time
        # since geom_path will use the first occurring observation
        # to color the corresponding segment. Without the sorting
        # geom_path will pick up NA values (corresponding to initial time
        # points); breaking the example.
        data = subset(
            data.frame(reducedDim(tse), colData(tse)),
            subject %in% top.selected.subjects) %>%
        arrange(desc(time)),
        # arrow end is reversed, due to the earlier sorting.
        arrow=arrow(length = unit(0.1, "inches"), ends = "first")) +
        labs(title = "Top 10%  divergent trajectories from time point one to two") +
        scale_color_gradient2(low="white", high="red")+
        theme(plot.title = element_text(hjust = 0.5))

Plotting an example of the trajectory with the maximum total divergence:

    # Get subject with the maximum total divergence
    selected.subject <- data.frame(reducedDim(tse), colData(tse)) %>%
        group_by(subject) %>%
        summarise(total_divergence = sum(time_divergence, na.rm = TRUE)) %>%
        filter(total_divergence==max(total_divergence)) %>% select(subject) %>%
        .[[1]]
    # plot
    p +  geom_path(
        aes(x=X1, y=X2, group=subject),
        data = subset(
            data.frame(reducedDim(tse), colData(tse)),
            subject %in% selected.subject) %>% arrange(time),
        arrow=arrow(length = unit(0.1, "inches"))) +
        labs(title = "Longest trajectory by divergence") +
        theme(plot.title = element_text(hjust = 0.5))

More examples and materials are available at Orchestrating Microbiome Analysis (Lahti et al. 2021).

# Session info

    sessionInfo()
    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] patchwork_1.3.2                 scater_1.41.1
    #>  [3] scuttle_1.23.0                  miaViz_1.21.0
    #>  [5] mia_1.21.0                      TreeSummarizedExperiment_2.21.0
    #>  [7] Biostrings_2.81.1               XVector_0.53.0
    #>  [9] SingleCellExperiment_1.35.0     MultiAssayExperiment_1.39.0
    #> [11] SummarizedExperiment_1.43.0     Biobase_2.73.1
    #> [13] GenomicRanges_1.65.0            Seqinfo_1.3.0
    #> [15] IRanges_2.47.0                  S4Vectors_0.51.1
    #> [17] BiocGenerics_0.59.0             generics_0.1.4
    #> [19] MatrixGenerics_1.25.0           matrixStats_1.5.0
    #> [21] ggraph_2.2.2                    ggplot2_4.0.3
    #> [23] BiocStyle_2.41.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3          sys_3.4.3
    #>   [3] jsonlite_2.0.0              magrittr_2.0.5
    #>   [5] ggbeeswarm_0.7.3            farver_2.1.2
    #>   [7] rmarkdown_2.31              fs_2.1.0
    #>   [9] vctrs_0.7.3                 memoise_2.0.1
    #>  [11] DelayedMatrixStats_1.35.0   ggtree_4.3.0
    #>  [13] htmltools_0.5.9             S4Arrays_1.13.0
    #>  [15] BiocNeighbors_2.7.1         janeaustenr_1.0.0
    #>  [17] SparseArray_1.13.2          gridGraphics_0.5-1
    #>  [19] sass_0.4.10                 bslib_0.10.0
    #>  [21] tokenizers_0.3.0            htmlwidgets_1.6.4
    #>  [23] plyr_1.8.9                  DECIPHER_3.9.0
    #>  [25] cachem_1.1.0                buildtools_1.0.0
    #>  [27] igraph_2.3.1                lifecycle_1.0.5
    #>  [29] pkgconfig_2.0.3             rsvd_1.0.5
    #>  [31] Matrix_1.7-5                R6_2.6.1
    #>  [33] fastmap_1.2.0               digest_0.6.39
    #>  [35] aplot_0.2.9                 tidytext_0.4.3
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    #>  [39] SnowballC_0.7.1             vegan_2.7-3
    #>  [41] beachmat_2.29.0             labeling_0.4.3
    #>  [43] polyclip_1.10-7             abind_1.4-8
    #>  [45] mgcv_1.9-4                  compiler_4.6.0
    #>  [47] fontquiver_0.2.1            withr_3.0.2
    #>  [49] S7_0.2.2                    BiocParallel_1.47.0
    #>  [51] viridis_0.6.5               DBI_1.3.0
    #>  [53] ggforce_0.5.0               MASS_7.3-65
    #>  [55] rappdirs_0.3.4              DelayedArray_0.39.1
    #>  [57] bluster_1.23.0              permute_0.9-10
    #>  [59] tools_4.6.0                 vipor_0.4.7
    #>  [61] beeswarm_0.4.0              ape_5.8-1
    #>  [63] glue_1.8.1                  nlme_3.1-169
    #>  [65] grid_4.6.0                  cluster_2.1.8.2
    #>  [67] reshape2_1.4.5              gtable_0.3.6
    #>  [69] tidyr_1.3.2                 BiocSingular_1.29.0
    #>  [71] tidygraph_1.3.1             ScaledMatrix_1.21.0
    #>  [73] ggrepel_0.9.8               pillar_1.11.1
    #>  [75] stringr_1.6.0               yulab.utils_0.2.4
    #>  [77] splines_4.6.0               dplyr_1.2.1
    #>  [79] tweenr_2.0.3                treeio_1.37.0
    #>  [81] lattice_0.22-9              tidyselect_1.2.1
    #>  [83] DirichletMultinomial_1.55.0 fontLiberation_0.1.0
    #>  [85] maketools_1.3.2             knitr_1.51
    #>  [87] fontBitstreamVera_0.1.1     gridExtra_2.3
    #>  [89] xfun_0.57                   graphlayouts_1.2.3
    #>  [91] stringi_1.8.7               lazyeval_0.2.3
    #>  [93] ggfun_0.2.0                 yaml_2.3.12
    #>  [95] evaluate_1.0.5              codetools_0.2-20
    #>  [97] gdtools_0.5.0               tibble_3.3.1
    #>  [99] BiocManager_1.30.27         ggplotify_0.1.3
    #> [101] cli_3.6.6                   systemfonts_1.3.2
    #> [103] jquerylib_0.1.4             Rcpp_1.1.1-1.1
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    #> [107] sparseMatrixStats_1.25.0    decontam_1.33.0
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    #> [113] purrr_1.2.2                 crayon_1.5.3
    #> [115] rlang_1.2.0                 cowplot_1.2.0

# References

Holmes, Ian, Keith Harris, and Christopher Quince. 2012. “Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics.” PLOS ONE. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030126.
Lahti, Leo, Sudarshan Shetty, and Felix GM Ernst. 2021. Orchestrating Microbiome Analysis. https://microbiome.github.io/OMA/.