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Advanced Visualization

Xiaotao Shen

2026-03-04

Source: vignettes/advanced_visualization.Rmd
advanced_visualization.Rmd

This article focuses on heavier visualization layers that go beyond the core overview plots: interactive trees, pathway enrichment summaries, and standardized longitudinal mixed-effect result plots.

library(microbiomedataset)

data("global_patterns", package = "microbiomedataset")
data("demo_crossomics", package = "microbiomedataset")

tree_object <- align_tree(global_patterns, tree = "taxa_tree")
microbiome_object <- demo_crossomics$microbiome_data
metabolome_object <- demo_crossomics$metabolome_data
sample_link <- demo_crossomics$sample_link

Interactive tree panels 

plot_tree_abundance(
  tree_object,
  tree = "taxa_tree",
  taxonomic_rank = "Phylum"
)

Static taxonomy tree used as the basis for the interactive tree panel.

plot_tree_interactive(
  tree_object,
  tree = "taxa_tree",
  taxonomic_rank = "Phylum"
)

Pathway enrichment 

association <- calculate_correlation(
  microbiome_data = microbiome_object,
  metabolome_data = metabolome_object,
  sample_link = sample_link,
  microbiome_rank = "Genus",
  metabolome_transform = "none"
)
pathway_link <- standardize_pathway_link(
  data.frame(
    taxon_id = summarise_taxa(
      microbiome_object,
      taxonomic_rank = "Genus"
    )@variable_info$variable_id[1],
    metabolite_id = metabolome_object@annotation_table$variable_id[1],
    pathway_id = "pathway_a",
    pathway_name = "Pathway A",
    stringsAsFactors = FALSE
  )
)
mechanism <- infer_metabolic_link(
  microbiome_data = microbiome_object,
  metabolome_data = metabolome_object,
  sample_link = sample_link,
  pathway_link = pathway_link,
  association_result = association,
  microbiome_rank = "Genus",
  q_value_cutoff = 1
)
enrichment_tbl <- calculate_pathway_enrichment(mechanism)
enrichment_tbl
#> # A tibble: 1 × 7
#>   pathway_id pathway_name n_background n_observed p_value q_value
#>   <chr>      <chr>               <int>      <int>   <dbl>   <dbl>
#> 1 pathway_a  Pathway A               1          1       1       1
#> # ℹ 1 more variable: enrichment_ratio <dbl>

Bubble plot summarizing pathway enrichment from mechanism-linked metabolites.

Longitudinal mixed-effect result plots 

mixed_effect_result <- create_longitudinal_mixed_effect_result(
  result = data.frame(
    feature = paste0("Taxon_", 1:12),
    term = rep(c("age", "study_groupcontrol"), each = 6),
    estimate = c(seq(-0.5, 0.6, length.out = 6), seq(0.4, -0.4, length.out = 6)),
    lower_ci = c(seq(-0.7, 0.4, length.out = 6), seq(0.2, -0.6, length.out = 6)),
    upper_ci = c(seq(-0.3, 0.8, length.out = 6), seq(0.6, -0.2, length.out = 6)),
    p_value = seq(0.001, 0.12, length.out = 12),
    stringsAsFactors = FALSE
  ),
  method = "external_demo",
  time_variable = "age",
  group_variable = "study_group"
)
mixed_effect_result
#> longitudinal_mixed_effect_result
#> Method: external_demo
#> Time variable: age
#> Group variable: study_group
#> Rows: 12
plot_longitudinal_mixed_effect(mixed_effect_result, top_n = 10)

Forest plot of longitudinal mixed-effect estimates for top taxa.

plot_longitudinal_effect_heatmap(mixed_effect_result, top_n = 10)

Heatmap of longitudinal mixed-effect estimates across features and model terms.

These advanced layers are designed to stay compatible with the same object system used elsewhere in the package, while still allowing external modeling results to be standardized into common plotting entry points.