Plot Comparison Boxplots Across Simulators Plot Benchmark Metric Score Distributions Across Simulators

Generates 600 DPI boxplots with jittered points showing the distribution of evaluation metric scores across simulated single-cell and multiomics datasets. Supports standardized direction-aligned fidelity scores (where higher is universally superior, resolving metric polarity differences across distance and correlation measures) or original unnormalized raw values with optional direction separation or per-metric faceting.

Usage

plot_metric_boxplots(
  benchmark_data,
  categories = NULL,
  metrics = NULL,
  score_type = c("normalized", "raw"),
  separate_direction = FALSE,
  facet_by = c("category", "metric"),
  palette = NULL,
  ncol = 4,
  base_size = 11
)

Arguments

benchmark_data

A data frame (such as demo$benchmark_summary_table or output from evaluate_simulation_accuracy()) or a named list of benchmark result tables.

categories

Optional character vector of categories to include. Default NULL (all 8 categories).

metrics

Optional character vector of specific metrics to include. Default NULL (all).

score_type

Character. Score formulation: "normalized" (default; direction-inverted standardized fidelity scores in [0, 1] where higher is universally superior across all measures) or "raw" (unnormalized original metric values).

separate_direction

Logical. When score_type = "raw", whether to separate metrics by optimization direction (lower vs higher is better) into distinct sub-panels. Default FALSE.

facet_by

Character. Facet layout: "category" (default; 8 canonical category panels) or "metric" (independent panels for each individual metric).

palette

Optional named character vector of simulator colors.

ncol

Integer. Number of columns in facet wrap. Default 4.

base_size

Numeric. Base font size. Default 11.

Value

A ggplot object.

Details

Examples

data(example_scrna, package = "scSimEval")
res <- evaluate_simulation_accuracy(example_scrna$ref, example_scrna$sim)
#> [1/5] Extracting cell-level properties...
#> [2/5] Extracting feature-level properties...
#> [3/5] Computing univariate accuracy metrics...
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log'; comparing: 2 vectors
#> [4/5] Computing bivariate metrics (Fasano-Franceschini, Peacock, KDE zstat, 2D EMD)...
#> Warning: argument is not numeric or logical: returning NA
#> Warning: emd: Maximum number of iterations has been reached (500)
#> Warning: argument is not numeric or logical: returning NA
#> Warning: emd: Maximum number of iterations has been reached (500)
#> Warning: argument is not numeric or logical: returning NA
#> Warning: emd: Maximum number of iterations has been reached (500)
#> Warning: argument is not numeric or logical: returning NA
#> Warning: emd: Maximum number of iterations has been reached (500)
#> [5/5] Computing zero-probability and manifold distances...
#> Unimodal accuracy evaluation complete.