library(phyloseq)
library(metacoder)

data(GlobalPatterns)
# Subsetting the dataset to keep only 2 sample types
GP_sub <- subset_samples(GlobalPatterns, (SampleType=='Ocean' | SampleType=='Soil'))
GP_sub <- prune_taxa(taxa_sums(GP_sub)>0, GP_sub)

# Agglomerating the dataset to Class 
GP_sub_class_glom <- tax_glom(GP_sub,taxrank="Class",NArm = F)
  


meta_obj <- parse_phyloseq(GP_sub_class_glom) 

meta_obj$data$otu_relab <- calc_obs_props(meta_obj, "otu_table")  

meta_obj$data$tax_relab <- calc_taxon_abund(meta_obj, "otu_relab") 

meta_obj$data$diff_table <- compare_groups(meta_obj, data = "tax_relab",
                                           cols = meta_obj$data$sample_data$sample_id,
                                           groups = meta_obj$data$sample_data$SampleType)

heat_tree(meta_obj,
          node_size = n_obs , 
          node_label = taxon_names,
          node_color = log2_median_ratio, # A column from `obj$data$diff_table`
          node_color_range = diverging_palette(), # The built-in palette for diverging data                 
          node_color_axis_label = "Log2 ratio median proportions",
          repel_labels = TRUE,
          layout = "davidson-harel", # The primary layout algorithm
          initial_layout = "reingold-tilford") # The layout algorithm that initializes node locations