plot_feature_overall_stability_incremental: Overall Feature Stability Incremental Boxplot

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Overall Feature Stability Incremental Boxplot

Description

Perform an incremental ECS between two consecutive feature steps. The ECS values are extracted for every resolution value and summarized using a function (e.g. median, mean, etc.).

Usage

plot_feature_overall_stability_incremental(
  feature_object_list,
  summary_function = stats::median,
  dodge_width = 0.7,
  text_size = 4,
  boxplot_width = 0.4,
  return_df = FALSE
)

Arguments

feature_object_list	An object or a concatenation of objects returned by the assess_feature_stability method.
summary_function	The function used to summarize the ECS values. Default is median.
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.
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.
return_df	If TRUE, the function will return the ECS values as a dataframe. Default is FALSE.

Value

A ggplot2 object with ECS distribution will be displayed as a boxplot. Above each boxplot there will be a pair of numbers representing the two steps that are compared.

Examples

set.seed(2024)
# create an artificial expression matrix
expr_matrix <- matrix(
    c(runif(50 * 10), runif(50 * 10, min = 3, max = 4)),
    nrow = 100, byrow = TRUE
)
rownames(expr_matrix) <- as.character(1:100)
colnames(expr_matrix) <- paste("feature", 1:10)

feature_stability_result <- assess_feature_stability(
    data_matrix = t(expr_matrix),
    feature_set = colnames(expr_matrix),
    steps = c(5, 10),
    feature_type = "feature_name",
    resolution = c(0.1, 0.5),
    n_repetitions = 3,
    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_overall_stability_incremental(feature_stability_result)

Core-Bioinformatics/ClustAssess documentation built on Nov. 4, 2024, 1:05 p.m.