library(metacoder)
meta <- dplyr::ungroup(hmp_samples)[hmp_samples$body_site %in% c('Stool', 'Throat'), ]
x <- parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
class_key = c(tax_rank = "taxon_rank", tax_name = "taxon_name"),
class_regex = "^(.+)__(.+)$")
# Convert counts to proportions
x$data$otu_table <- calc_obs_props(x, data = "tax_data", cols = meta$sample_id)
# Get per-taxon counts
x$data$tax_table <- calc_taxon_abund(x, data = "otu_table", cols = meta$sample_id)
# Calculate difference between treatments
x$data$diff_table <- compare_groups(x, data = "tax_table",
cols = meta$sample_id,
groups = meta$body_site)
heat_tree_matrix(x,
data = "diff_table",
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_range = diverging_palette(),
node_color_trans = "linear",
node_color_interval = c(-3, 3),
edge_color_interval = c(-3, 3),
node_size_axis_label = "Number of OTUs",
node_color_axis_label = "Log2 ratio median proportions")