plot_2D_intervals: Plot 2D Intervals for Mean-Variance Association

In stemangiola/sccomp: Tests differences in cell-type proportion for single-cell data, robust to outliers

Plot 2D Intervals for Mean-Variance Association

Description

This function creates a 2D interval plot for mean-variance association, highlighting significant differences based on a given significance threshold.

Usage

plot_2D_intervals(
  .data,
  significance_threshold = 0.05,
  test_composition_above_logit_fold_change = attr(.data,
    "test_composition_above_logit_fold_change")
)

Arguments

.data	Data frame containing the main data.
significance_threshold	Numeric value specifying the significance threshold for highlighting differences. Default is 0.025.
test_composition_above_logit_fold_change	A positive integer. It is the effect threshold used for the hypothesis test. A value of 0.2 correspond to a change in cell proportion of 10% for a cell type with baseline proportion of 50%. That is, a cell type goes from 45% to 50%. When the baseline proportion is closer to 0 or 1 this effect thrshold has consistent value in the logit uncontrained scale.

Value

A ggplot object representing the 2D interval plot.

Examples

print("cmdstanr is needed to run this example.")


  if (instantiate::stan_cmdstan_exists()) {
    data("counts_obj")

    estimate <- sccomp_estimate(
      counts_obj,
      ~ type,
      ~type,
      sample,
      cell_group,
      count,
      cores = 1
    ) |> 
    sccomp_test()
    
  # Example usage:
  my_plot = plot_2D_intervals(estimate)
    
  }

stemangiola/sccomp documentation built on June 1, 2025, 7:03 p.m.