plot_ulrb_silhouette function

Plot silhouette scores from clustering results

Plots the Silhouette scores from the clustering results of define_rb().

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

Arguments

• data: ...
• sample_id: string with name of selected sample.
• taxa_col: string with name of column with phylogenetic units. Usually OTU or ASV.
• samples_col: name of column with sample ID's.
• 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.
• classification_col: string with name of column with classification for each row. Default value is "Classification".
• silhouette_score: string with column name with silhouette score values. Default is "Silhouette_scores"
• colors: vector with colors. Should have the same lenght as the number of classifications.
• log_scaled: if TRUE then abundance scores will be shown in Log10 scale. Default to FALSE.
• ...: other arguments.

Returns

A ggplot object of Silhouette plot obtained from the selected sample.

Details

This works as a sanity check of the results obtained by the unsupervised learning method used to classify taxa. This is specially important if you used an automatic number of clusters.

The function works for either a single sample (that you specify with sample_id argument), or it can apply a centrality metric for taxa across all your samples (plot_all = TRUE).

For more details on Silhouette score, see check_avgSil() and cluster::silhouette().

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)

# Standard plot for a single sample
plot_ulrb_silhouette(classified_species,
                       sample_id = "ERR2044669",
                       taxa_col = "OTU",
                       abundance_col = "Abundance",
                       plot_all = FALSE)
# All samples in a dataset
plot_ulrb_silhouette(classified_species,
          taxa_col = "OTU",
          abundance_col = "Abundance",
          plot_all = TRUE)

# All samples with a log scale
plot_ulrb_silhouette(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(), cluster::silhouette(), cluster::pam()