plot.sccomp_tbl: plot

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

plot

Description

This function plots a summary of the results of the model.

Usage

## S3 method for class 'sccomp_tbl'
plot(
  x,
  significance_threshold = 0.05,
  test_composition_above_logit_fold_change = attr(.data,
    "test_composition_above_logit_fold_change"),
  ...
)

Arguments

x	A tibble including a cell_group name column | sample name column | read counts column | factor columns | Pvalue column | a significance column
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.
...	For internal use

Value

A ggplot

Examples

message("Use the following example after having 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,
      ~ type, ~1, sample, cell_group, count,
      cores = 1
    )

    # estimate |> plot()
  }

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