plot_feature_per_resolution_stability_boxplot: Per resolution Feature Stability Boxplot

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Per resolution Feature Stability Boxplot

Description

Display EC consistency for each feature set and for each step. Above each boxplot there is a number representing the step (or the size of the subset). The ECC values are extracted depdening on the resolution value provided by the user.

Usage

plot_feature_per_resolution_stability_boxplot(
  feature_object_list,
  resolution,
  violin_plot = FALSE,
  text_size = 4,
  boxplot_width = 0.4,
  dodge_width = 0.7,
  return_df = FALSE
)

Arguments

feature_object_list	An object or a concatenation of objects returned by the assess_feature_stability method
resolution	The resolution value for which the ECC will be extracted.
violin_plot	If TRUE, the function will return a violin plot instead of a boxplot. Default is FALSE.
text_size	The size of the labels above boxplots
boxplot_width	Used for adjusting the width of the boxplots; the value will be passed to the width argument of the ggplot2::geom_boxplot method.
dodge_width	Used for adjusting the horizontal position of the boxplot; the value will be passed to the width argument of the ggplot2::position_dodge method.
return_df	If TRUE, the function will return the ECS values as a dataframe. Default is FALSE.

Value

A ggplot2 object.

Examples

set.seed(2024)
# create an artificial expression matrix
expr_matrix <- matrix(
    c(runif(100 * 10), runif(100 * 10, min = 3, max = 4)),
    nrow = 200, byrow = TRUE
)
rownames(expr_matrix) <- as.character(1:200)
colnames(expr_matrix) <- paste("feature", 1:10)

feature_stability_result <- assess_feature_stability(
    data_matrix = t(expr_matrix),
    feature_set = colnames(expr_matrix),
    steps = 5,
    feature_type = "feature_name",
    resolution = c(0.1, 0.5, 1),
    n_repetitions = 10,
    umap_arguments = list(
        # the following parameters are used by the umap function
        # and are not mandatory
        n_neighbors = 3,
        approx_pow = TRUE,
        n_epochs = 0,
        init = "random",
        min_dist = 0.3
    ),
    clustering_algorithm = 1
)
plot_feature_per_resolution_stability_boxplot(feature_stability_result, 0.5)

Core-Bioinformatics/ClustAssess documentation built on Nov. 14, 2024, 6:33 p.m.