Differences in the Community Composition due to Diet • phylobar

Diet is a key factor shaping the gut microbiome. In a recent mouse study, researchers profiled gut microbial communities under different dietary conditions to examine how nutrient intake influences community structure. The dataset, available as a phyloseq object (a Bioconductor container for microbiome data), includes microbial feature tables, taxonomic annotations, and sample metadata.

In this vignette, we use these data to demonstrate how phylobar can produce phylogenetically informed visualizations of microbiome composition. We begin by normalizing feature abundances and reconstructing the taxonomic tree. We then generate a phylobar plot to highlight community shifts across diet groups, with a focus on clades where dietary effects are most pronounced.

library(phylobar)
library(phyloseq)
library(DESeq2)
library(MicrobiomeStat)

Setup

We begin by loading the mouse diet study data, which is stored remotely and can be loaded into R as a phyloseq object.

data("dietswap", package = "microbiome")
diet_temp <- subset_samples(dietswap, timepoint == 1)
diet <- subset_taxa(diet_temp, taxa_sums(diet_temp) > 0)

To prepare the data for visualization, we first convert the phyloseq object into a MicrobiomeStat object using mStat_convert_phyloseq_to_data_obj(). This enables us to apply DESeq2 normalization, which corrects for sequencing depth differences across samples. We then transpose the normalized feature matrix so that rows correspond to taxa and columns to samples, as expected by phylobar().

Unlike relative-abundance normalization, DESeq2 does not constrain sample totals to equal one. This distinction highlights the flexibility of our functions, since phylobar can accommodate both compositional and non-compositional inputs. After normalization, we transpose the feature matrix so that rows correspond to taxa and columns to samples, as required by phylobar().

diet_mstat <- mStat_convert_phyloseq_to_data_obj(diet)
diet_mstat <- mStat_normalize_data(diet_mstat, "DESeq")
x <- t(diet_mstat$data.obj.norm$feature.tab)

Next, we construct a taxonomy-based tree from the available annotations. To ensure that ancestor and descendant nodes remain uniquely identifiable, we add prefixes to each taxonomic level (e.g., p_ for phylum, f_ for family). Without this step, different levels can share the same names, which would prevent the taxonomy from being converted into a valid tree. The prefixed taxonomy table is then passed to taxonomy_to_tree(), producing a hierarchical tree that will serve as the backbone for visualization.

taxa <- tax_table(diet) |>
    phylobar::add_prefix()
taxa <- cbind(Kingdom = "k_Bacteria", taxa)
tree <- taxonomy_to_tree(taxa)

At this point, we have two aligned objects ready for visualization: a normalized abundance matrix (x) and a taxonomy-derived tree (tree). We now pass these objects to phylobar(). The resulting plot shows stacked bar charts of taxa abundances, aligned with the hierarchical tree structure.

phylobar(x, tree, width = 800)

Interpretation

We showcase several notable patterns using screenshots from the interactive plots: most mice display a co-dominance of Firmicutes (purple) and Bacteroidetes (teal), but sample_52 shows a clear Firmicutes dominance, underscoring how dietary interventions can shift the balance between these two phyla.

Next, we observe the abundance of the Clostridium cluster IV family acros samples. The variability in its abundance suggests that while Clostridium cluster IV is widespread, its contribution to community structure differs across individuals or conditions.

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] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] MicrobiomeStat_1.5.0        tibble_3.3.1               
#>  [3] rlang_1.1.7                 DESeq2_1.48.2              
#>  [5] SummarizedExperiment_1.38.1 Biobase_2.68.0             
#>  [7] MatrixGenerics_1.20.0       matrixStats_1.5.0          
#>  [9] GenomicRanges_1.60.0        GenomeInfoDb_1.44.3        
#> [11] IRanges_2.42.0              S4Vectors_0.48.0           
#> [13] BiocGenerics_0.54.1         generics_0.1.4             
#> [15] phyloseq_1.52.0             phylobar_0.99.0            
#> [17] BiocStyle_2.36.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] Rdpack_2.6.6            phangorn_2.12.1         permute_0.9-10         
#>   [4] magrittr_2.0.4          clue_0.3-66             ade4_1.7-23            
#>   [7] otel_0.2.0              compiler_4.5.1          mgcv_1.9-4             
#>  [10] systemfonts_1.3.1       vctrs_0.7.1             reshape2_1.4.5         
#>  [13] rmutil_1.1.10           quadprog_1.5-8          stringr_1.6.0          
#>  [16] pkgconfig_2.0.3         crayon_1.5.3            fastmap_1.2.0          
#>  [19] XVector_0.48.0          pander_0.6.6            modeest_2.4.0          
#>  [22] rmarkdown_2.30          nloptr_2.2.1            UCSC.utils_1.4.0       
#>  [25] ragg_1.5.0              purrr_1.2.1             xfun_0.56              
#>  [28] cachem_1.1.0            jsonlite_2.0.0          biomformat_1.36.0      
#>  [31] rhdf5filters_1.20.0     DelayedArray_0.34.1     Rhdf5lib_1.30.0        
#>  [34] BiocParallel_1.42.2     parallel_4.5.1          cluster_2.1.8.2        
#>  [37] R6_2.6.1                bslib_0.10.0            stringi_1.8.7          
#>  [40] RColorBrewer_1.1-3      rpart_4.1.24            boot_1.3-32            
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#>  [61] withr_3.0.2             S7_0.2.1                stable_1.1.7           
#>  [64] evaluate_1.0.5          desc_1.4.3              survival_3.8-6         
#>  [67] Biostrings_2.76.0       pillar_1.11.1           BiocManager_1.30.27    
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