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Plot ulrb clustering results and silhouette scores

Source: R/plot_ulrb.R
plot_ulrb.Rd

Function to help access clustering results from ulrb.

Usage

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

Value

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).

See also

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

Examples

# \donttest{
classified_species <- define_rb(nice_tidy)
#> Joining with `by = join_by(Sample, Level)`

# Default parameters for a single sample ERR2044669
plot_ulrb(classified_species,
          sample_id = "ERR2044669",
          taxa_col = "OTU",
          abundance_col = "Abundance")
#> Warning: If you want to plot only ERR2044669 use plot_all = FALSE
#> Warning: If you want to plot only ERR2044669 use plot_all = FALSE


# 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)

# }