plotting anno_tax_density with 2 groups? #214

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1. david-barnett commented on Mar 10, 2026

   

   david-barnett

   on Mar 10, 2026

   Owner

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   These heatmaps and annotations are made with ComplexHeatmap, there doesn't seem to be a native way to group the density plots.

   https://jokergoo.github.io/ComplexHeatmap-reference/book/heatmap-annotations.html#density-annotation

   I had an AI have a go at rolling something custom that meets your needs, and this solution does seem to work and the approach makes sense but it's not a straightforward piece of code, so please check carefully that the densities match your side-by-side version :)

   • sadly i don't have time to give this further human attention myself, good luck!

   library(microViz)
   library(phyloseq)
   library(dplyr)
   library(ComplexHeatmap)
   library(grid)
   
   data("ibd", package = "microViz")
   
   # Prepare an example object similar to the microViz heatmaps docs
   psq <- ibd %>%
     tax_filter(min_prevalence = 5) %>%
     tax_mutate(Species = NULL) %>%
     tax_fix() %>%
     tax_agg("Genus")
   
   # Pick the two most frequent DiseaseState levels
   sd <- data.frame(sample_data(psq))
   grps <- names(sort(table(sd$DiseaseState), decreasing = TRUE))[1:2]
   psq_a <- subset_samples(psq, DiseaseState == grps[1])
   psq_b <- subset_samples(psq, DiseaseState == grps[2])
   
   # Choose taxa to display
   taxa <- tax_top(psq, n = 20, rank = "Genus")
   
   # Transformation for the density annotation (matches microViz heatmaps style)
   trans_fun <- function(ps) {
     ps %>%
       tax_transform("compositional", zero_replace = 1) %>%
       tax_transform("log2", zero_replace = "halfmin", chain = TRUE)
   }
   
   # Compute shared x-axis limits after applying the same transform and "only_detected" rule
   calc_x <- function(ps, taxa, undetected = 0, only_detected = TRUE) {
     otu_counts <- as(otu_table(ps), "matrix")
     if (taxa_are_rows(ps)) otu_counts <- t(otu_counts)
     keep <- if (only_detected) otu_counts[, taxa, drop = FALSE] > undetected else TRUE
   
     ps_t <- trans_fun(ps)
     otu_t <- as(otu_table(ps_t), "matrix")
     if (taxa_are_rows(ps_t)) otu_t <- t(otu_t)
   
     x <- otu_t[, taxa, drop = FALSE]
     x[!keep] <- NaN
     x
   }
   
   x_a <- calc_x(psq_a, taxa)
   x_b <- calc_x(psq_b, taxa)
   xlim_shared <- range(c(x_a, x_b), finite = TRUE)
   
   # Optional: enforce a shared vertical scale across the two layers (recommended for fair overlays)
   max_dens <- function(xmat) {
     apply(xmat, 2, function(v) {
       v <- v[is.finite(v)]
       if (length(v) < 2) return(NA_real_)
       max(stats::density(v)$y)
     })
   }
   max_density_shared <- max(c(max_dens(x_a), max_dens(x_b)), finite = TRUE)
   
   # Build two density layers with identical scales, then overlay them
   layer_a <- anno_tax_density(
     data = psq_a, taxa = taxa, which = "row",
     trans = trans_fun,
     xlim = xlim_shared,
     max_density = max_density_shared,
     gp = gpar(fill = "#F65A6D", alpha = 0.25),
     border = TRUE, axis = TRUE
   )
   
   layer_b <- anno_tax_density(
     data = psq_b, taxa = taxa, which = "row",
     trans = trans_fun,
     xlim = xlim_shared,
     max_density = max_density_shared,
     gp = gpar(fill = "#6a51a3", alpha = 0.25),
     border = FALSE, axis = FALSE
   )
   
   overlap_anno <- AnnotationFunction(
     fun = function(index, ...) {
       layer_a@fun(index, ...)
       layer_b@fun(index, ...)
     },
     var_import = list(layer_a = layer_a, layer_b = layer_b),
     which = "row",
     n = length(taxa),
     subsettable = TRUE,
     width = unit(30, "mm")
   )
   
   # Draw a composition heatmap with the overlaid density tax annotation
   psq %>%
     tax_transform("compositional", zero_replace = 1) %>%
     comp_heatmap(
       taxa = taxa,
       tax_anno = taxAnnotation(
         "test" = overlap_anno
       )
     )

   
2. Repository owner locked and limited conversation to collaborators on Mar 10, 2026
3. david-barnett

   converted this issue into a discussion #215 on Mar 10, 2026