plot_2D_intervals: Plot 2D Intervals for Mean-Variance Association

In stemangiola/sccomp: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data

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

# Example usage:
# plot_2D_intervals(.data, "cell_group", theme_minimal(), 0.025)

stemangiola/sccomp documentation built on Dec. 20, 2024, 8:38 a.m.