Skip to contents

Cross-Omics Visualization

Xiaotao Shen

2026-03-04

Source: vignettes/visualization_crossomics.Rmd
visualization_crossomics.Rmd

This article collects visualization helpers for correlation, network, mechanism, and differential-abundance result objects. These functions build on top of paired microbiome_dataset and mass_dataset inputs but plot standardized result classes.

library(microbiomedataset)

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

microbiome_object <- demo_crossomics$microbiome_data
metabolome_object <- demo_crossomics$metabolome_data
sample_link <- demo_crossomics$sample_link

correlation <- calculate_correlation(
  microbiome_data = microbiome_object,
  metabolome_data = metabolome_object,
  sample_link = sample_link,
  microbiome_rank = "Genus",
  metabolome_transform = "none"
)

correlation_network <- build_correlation_network(
  correlation,
  min_abs_correlation = 0.2,
  max_q_value = 1,
  top_n = 20
)

Correlation and modules 

plot_correlation_heatmap(correlation, top_n_taxa = 8, top_n_metabolites = 8)

Correlation heatmap of top taxa and metabolites.

plot_network_modules(correlation_network)

Correlation network with nodes colored by module membership and block type.

plot_module_summary(correlation_network)

Barplot summarizing module sizes in the cross-omics correlation network.

Mechanism summaries 

genus_object <- summarise_taxa(microbiome_object, taxonomic_rank = "Genus")
pathway_link <- standardize_pathway_link(
  data.frame(
    taxon_id = genus_object@variable_info$variable_id[1],
    metabolite_id = metabolome_object@annotation_table$variable_id[1],
    pathway_id = "example_pathway",
    pathway_name = "Example pathway",
    stringsAsFactors = FALSE
  )
)
mechanism <- infer_metabolic_link(
  microbiome_data = microbiome_object,
  metabolome_data = metabolome_object,
  sample_link = sample_link,
  pathway_link = pathway_link,
  association_result = correlation,
  microbiome_rank = "Genus",
  q_value_cutoff = 1
)
plot_mechanism_network(mechanism, top_n = 10)

Taxon-pathway-metabolite mechanism network.

plot_pathway_summary(mechanism, top_n = 10)

Pathway summary ranked by mechanism score.

Differential abundance result views 

da_table <- genus_object@variable_info[1:20, c("variable_id", "Kingdom", "Phylum", "Class", "Order", "Family", "Genus"), drop = FALSE]
da_table$estimate <- seq(-2, 2, length.out = nrow(da_table))
da_table$p_value <- seq(0.001, 0.2, length.out = nrow(da_table))
da_table$q_value <- p.adjust(da_table$p_value, method = "BH")
da_result <- create_differential_abundance_result(
  result = da_table,
  method = "external_demo",
  taxonomic_rank = "Genus",
  group = "group_1_vs_group_0"
)

Volcano plot of differential abundance results.

plot_differential_effect_size(da_result, top_n = 12)

Effect size barplot for top differential taxa.

plot_differential_cladogram(da_result, top_n = 12)

Circular taxonomy cladogram for top differential taxa.

da_ancombc <- run_differential_abundance(
  object = prune_taxa(microbiome_object, variable_id = microbiome_object@variable_info$variable_id[1:40]),
  formula = "study_group",
  method = "ancombc",
  taxonomic_rank = "Genus"
)
da_ancombc
#> differential_abundance_result
#> Method: ancombc
#> Taxonomic rank: Genus
#> Group: study_group1
#> Rows: 40

These result-focused plots sit on top of standardized result classes, so the same plotting API can be reused for external results and native workflows. For interactive tree panels, pathway enrichment views, and longitudinal mixed-effect result plots, see the advanced visualization article.