plot_ulrb function

Plot ulrb clustering results and silhouette scores

Function to help access clustering results from ulrb.

plot_ulrb(
  data,
  sample_id = NULL,
  taxa_col,
  plot_all = TRUE,
  silhouette_score = "Silhouette_scores",
  classification_col = "Classification",
  abundance_col = "Abundance",
  log_scaled = FALSE,
  colors = c("#009E73", "grey41", "#CC79A7"),
  ...
)

Arguments

• data: a data.frame with, at least, the classification, abundance and sample information for each phylogenetic unit.
• sample_id: string with name of selected sample.
• taxa_col: string with name of column with phylogenetic units. Usually OTU or ASV.
• plot_all: If TRUE, will make a plot for all samples with mean and standard deviation. If FALSE (default), then the plot will illustrate a single sample, that you have to specifiy in sample_id argument.
• silhouette_score: string with column name with silhouette score values. Default is "Silhouette_scores"
• classification_col: string with name of column with classification for each row. Default value is "Classification".
• abundance_col: string with name of column with abundance values. Default is "Abundance".
• log_scaled: if TRUE then abundance scores will be shown in Log10 scale. Default to FALSE.
• colors: vector with colors. Should have the same lenght as the number of classifications.
• ...: other arguments

Returns

A grid of ggplot objects with clustering results and silhouette plot obtained from define_rb().

Details

This function combined plot_ulrb_clustering() and plot_ulrb_silhouette(). The plots can be done for a single sample or for all samples.

The results from the main function of ulrb package, define_rb(), will include the classification of each taxa and the silhouette score obtained for each observation. Thus, to access the clustering results, there are two main plots to check:

• the rank abundance curve obtained after ulrb classification;
• and the silhouette plot.

Interpretation of Silhouette plot

Based on chapter 2 of "Finding Groups in Data: An Introduction to Cluster Analysis." (Kaufman and Rousseeuw, 1991); a possible interpretation of the clustering structure based on the Silhouette plot is:

• 0.71-1.00 (A strong structure has been found);
• 0.51-0.70 (A reasonable structure has been found);
• 0.26-0.50 (The structure is weak and could be artificial);
• <0.26 (No structure has been found).

Examples

classified_species <- define_rb(nice_tidy)

# Default parameters for a single sample ERR2044669
plot_ulrb(classified_species,
          sample_id = "ERR2044669",
          taxa_col = "OTU",
          abundance_col = "Abundance")

# All samples in a dataset
plot_ulrb(classified_species,
          taxa_col = "OTU",
          abundance_col = "Abundance",
          plot_all = TRUE)

# All samples with a log scale
plot_ulrb(classified_species,
          taxa_col = "OTU",
          abundance_col = "Abundance",
          plot_all = TRUE,
          log_scaled = TRUE)

See Also

define_rb(), check_avgSil(), plot_ulrb_clustering(), plot_ulrb_silhouette()