sccomp_boxplot: sccomp_boxplot

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

sccomp_boxplot

Description

Creates a boxplot visualization of the model results from sccomp. This function plots the estimated cell proportions across samples, highlighting significant changes in cell composition according to a specified factor.

Usage

sccomp_boxplot(
  .data,
  factor,
  significance_threshold = 0.05,
  test_composition_above_logit_fold_change = attr(.data,
    "test_composition_above_logit_fold_change"),
  remove_unwanted_effects = FALSE
)

Arguments

.data	A tibble containing the results from sccomp_estimate and sccomp_test, including the columns: cell_group name, sample name, read counts, factor(s), p-values, and significance indicators.
factor	A character string specifying the factor of interest included in the model for stratifying the boxplot.
significance_threshold	A numeric value indicating the False Discovery Rate (FDR) threshold for labeling significant cell-groups. Defaults to 0.05.
test_composition_above_logit_fold_change	A positive numeric value representing the effect size threshold used in the hypothesis test. A value of 0.2 corresponds to a change in cell proportion of approximately 10% for a cell type with a baseline proportion of 50% (e.g., from 45% to 55%). This threshold is consistent on the logit-unconstrained scale, even when the baseline proportion is close to 0 or 1.
remove_unwanted_effects	A logical value indicating whether to remove unwanted variation from the data before plotting. Defaults to FALSE.

Value

A ggplot object representing the boxplot of cell proportions across samples, stratified by the specified factor.

Examples

# Note: Before running the example, ensure that the 'cmdstanr' package is installed:
# install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))


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

    estimate <- sccomp_estimate(
      counts_obj,
      formula_composition = ~ type,
      formula_variability = ~ 1,
      .sample = sample,
      .cell_group = cell_group,
      .count = count,
      cores = 1
    ) %>%
    sccomp_test()

    # Plot the boxplot of estimated cell proportions
    sccomp_boxplot(
        .data = estimate,
        factor = "type",
        significance_threshold = 0.05
    )
}

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