The Human Intestinal Tract (HITChip) Atlas dataset is a widely used reference for studying variation in the gut microbiome across diverse individuals. The dataset is available in the microbiome package as atlas1006, and includes microbial abundances along with metadata such as extraction method, subject information, and taxonomic annotations.
In this vignette, we demonstrate how to use phylobar to create phylogeny-aware visualizations of the atlas data. We will walk through tree construction, highlight potential pitfalls in taxonomic labeling, and generate visualizations that summarize variation across samples.
library(ape)
library(phylobar)
library(microbiome)
#> Warning: package 'ggplot2' was built under R version 4.5.2Setup
We begin by loading the atlas data, which is available through the microbiome package as a phyloseq object (a Bioconductor container for microbiome data).
data(atlas1006)
pseq3 <- atlas1006 |>
subset_samples(DNA_extraction_method == "r") |>
transform(transform = "compositional")Next, we load the atlas1006 dataset. For simplicity, we focus on samples with the r DNA extraction method and transform abundances into compositional form (so each sample sums to 1).
tree <- taxonomy_to_tree(tax_table(pseq3))
checkValidPhylo(tree)
#> Starting checking the validity of tree...
#> Found number of tips: n = 128
#> Found number of nodes: m = 23
#> FATAL: nodes and tips should appear only once in the 2nd column of 'edge'
#> FATAL: the root node should not appear in the 2nd column of 'edge'
#> Done.A naive attempt to build a taxonomy-based tree can fail, because duplicate names across different levels of the taxonomy may result in loops. To address this, we add level-specific prefixes (e.g., p_ for phylum, f_ for family), ensuring that ancestor and descendant nodes have unique labels.
taxa <- tax_table(pseq3)
taxa <- cbind(Kingdom = "Bacteria", taxa)
taxa <- phylobar::add_prefix(taxa)
tree <- taxonomy_to_tree(taxa)
checkValidPhylo(tree)
#> Starting checking the validity of tree...
#> Found number of tips: n = 130
#> Found number of nodes: m = 31
#> Done.With a valid tree and abundance matrix, we can generate a phylobar plot. Here, samples are arranged along the x-axis, with stacked bars representing taxonomic abundances, aligned with the corresponding branches of the tree.
Because the full dataset is complex, we can simplify the visualization by subsampling representative samples. This preserves the main conclusions while reducing visual clutter.
x_sub <- subset_cluster(x)
x_sub <- x_sub[, colSums(x_sub) > 0]
leaves_to_keep <- intersect(tree$tip.label, colnames(x_sub))
filtered_tree <- drop.tip(tree, setdiff(tree$tip.label, leaves_to_keep))
phylobar(x_sub, filtered_tree)By default, the subset_cluster() function subsets to
samples that are close to a cut hierarchical clustering tree’s
centroids. Alternatively, setting method = "medoid" will
choose representatives using
-medoids
according to Bray-Curtis distance. The result is shown below, though our
interpretation will focus on the hierarchical clustering version.
Interpretation
There are many interesting patterns in the atlas dataset revealing distinct patterns in the human intestinal. For example, Enterobacter aerogenes appears across a broad range of samples, but generally at low relative abundance. Only one sample shows notable representation, creating a narrow band in the stacked barplot. This patchy distribution is a characteristic of opportunistic or conditionally rare taxa, which may be present in many individuals but rarely dominate the community structure. Such patterns have been previously been described as examples of bimodal or “tipping element” behavior, where a taxon is abundant in some hosts but nearly absent in others. (https://pmc.ncbi.nlm.nih.gov/articles/PMC4102116/pdf/ncomms5344.pdf)
Next, this screenshot highlights the dominance of Firmicutes (yellow) and Bacteroidetes (purple) in the human gut microbiome. While these two phyla are prevalent across nearly all samples, their relative proportions vary widely. Some samples are Firmicutes-dominant, while others are Bacteroidetes-dominant. This variability reflects the complex interplay of host genetics, diet, and environmental factors that shape the gut microbiome.
Session Info
sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
#>
#> time zone: America/Chicago
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] microbiome_1.30.0 ggplot2_4.0.2 phyloseq_1.52.0 phylobar_0.99.0
#> [5] ape_5.8-1 BiocStyle_2.36.0
#>
#> loaded via a namespace (and not attached):
#> [1] ade4_1.7-23 tidyselect_1.2.1 dplyr_1.2.0
#> [4] farver_2.1.2 Biostrings_2.76.0 S7_0.2.1
#> [7] fastmap_1.2.0 digest_0.6.39 lifecycle_1.0.5
#> [10] cluster_2.1.8.2 survival_3.8-6 magrittr_2.0.4
#> [13] compiler_4.5.1 rlang_1.1.7 sass_0.4.10
#> [16] tools_4.5.1 igraph_2.2.1 yaml_2.3.12
#> [19] data.table_1.18.2.1 knitr_1.51 phangorn_2.12.1
#> [22] htmlwidgets_1.6.4 plyr_1.8.9 RColorBrewer_1.1-3
#> [25] Rtsne_0.17 withr_3.0.2 purrr_1.2.1
#> [28] BiocGenerics_0.54.1 desc_1.4.3 grid_4.5.1
#> [31] stats4_4.5.1 multtest_2.64.0 biomformat_1.36.0
#> [34] Rhdf5lib_1.30.0 scales_1.4.0 iterators_1.0.14
#> [37] MASS_7.3-65 cli_3.6.5 vegan_2.7-2
#> [40] rmarkdown_2.30 crayon_1.5.3 ragg_1.5.0
#> [43] generics_0.1.4 otel_0.2.0 httr_1.4.7
#> [46] reshape2_1.4.5 cachem_1.1.0 rhdf5_2.52.1
#> [49] stringr_1.6.0 splines_4.5.1 parallel_4.5.1
#> [52] BiocManager_1.30.27 XVector_0.48.0 vctrs_0.7.1
#> [55] Matrix_1.7-4 jsonlite_2.0.0 bookdown_0.46
#> [58] IRanges_2.42.0 S4Vectors_0.48.0 systemfonts_1.3.1
#> [61] foreach_1.5.2 tidyr_1.3.2 jquerylib_0.1.4
#> [64] glue_1.8.0 pkgdown_2.2.0 codetools_0.2-20
#> [67] stringi_1.8.7 gtable_0.3.6 GenomeInfoDb_1.44.3
#> [70] UCSC.utils_1.4.0 quadprog_1.5-8 tibble_3.3.1
#> [73] pillar_1.11.1 htmltools_0.5.9 rhdf5filters_1.20.0
#> [76] GenomeInfoDbData_1.2.14 R6_2.6.1 textshaping_1.0.4
#> [79] evaluate_1.0.5 lattice_0.22-9 Biobase_2.68.0
#> [82] bslib_0.10.0 Rcpp_1.1.1 fastmatch_1.1-8
#> [85] permute_0.9-10 nlme_3.1-168 mgcv_1.9-4
#> [88] xfun_0.56 fs_1.6.6 pkgconfig_2.0.3