# NOT RUN {
# Parse dataset for plotting
x = parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
class_key = c(tax_rank = "info", tax_name = "taxon_name"),
class_regex = "^(.+)__(.+)$")
# Default appearance:
# No parmeters are needed, but the default tree is not too useful
heat_tree(x)
# A good place to start:
# There will always be "taxon_names" and "n_obs" variables, so this is a
# good place to start. This will shown the number of OTUs in this case.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs)
# Plotting read depth:
# To plot read depth, you first need to add up the number of reads per taxon.
# The function `calc_taxon_abund` is good for this.
x$data$taxon_counts <- calc_taxon_abund(x, dataset = "tax_data")
x$data$taxon_counts$total <- rowSums(x$data$taxon_counts[, -1]) # -1 = taxon_id column
heat_tree(x, node_label = taxon_names, node_size = total, node_color = total)
# Plotting multiple variables:
# You can plot up to 4 quantative variables use node/edge size/color, but it
# is usually best to use 2 or 3. The plot below uses node size for number of
# OTUs and color for number of reads and edge size for number of samples
x$data$taxon_counts <- calc_n_samples(x, dataset = "taxon_counts", append = TRUE)
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = total,
edge_color = n_samples)
# Different layouts:
# You can use any layout implemented by igraph. You can also specify an
# initial layout to seed the main layout with.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
layout = "davidson-harel")
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
layout = "davidson-harel", initial_layout = "reingold-tilford")
# Axis labels:
# You can add custom labeles to the legends
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = total,
edge_color = n_samples, node_size_axis_label = "Number of OTUs",
node_color_axis_label = "Number of reads",
edge_color_axis_label = "Number of samples")
# Overlap avoidance:
# You can change how much node overlap avoidance is used.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
overlap_avoidance = .5)
# Label overlap avoidance
# You can modfiy how label scattering is handled using the `replel_force` and
`repel_iter` options. You can turn off label scattering using the `repel_labels` option.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
repel_force = 2, repel_iter = 20000)
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
repel_labels = FALSE)
# Setting the size of graph elements:
# You can force nodes, edges, and lables to be a specific size/color range instead
# of letting the function optimize it. These options end in `_range`.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
node_size_range = c(0.01, .1))
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
edge_color_range = c("black", "#FFFFFF"))
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
node_label_size_range = c(0.02, 0.02))
# Setting the transformation used:
# You can change how raw statistics are converted to color/size using options
# ending in _trans.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
node_size_trans = "log10 area")
# Setting the interval displayed:
# By default, the whole range of the statistic provided will be displayed.
# You can set what range of values are displayed using options ending in `_interval`.
heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = n_obs,
node_size_interval = c(10, 100))
# }
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