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Plot Multi-Dimensional Computational Scalability Benchmark

Source: R/10_visualizations.R
plot_scalability_benchmark.Rd

Evaluates computational resource efficiency across single-cell simulators using raw benchmarking measurements (elapsed real wall-clock time, total CPU time, peak resident Plot Multi-Dimensional Computational Scalability Benchmark

Usage

plot_scalability_benchmark(
  benchmark_data,
  type = c("composite", "runtime", "memory", "tradeoff", "cost", "throughput",
    "efficiency"),
  layout = c("4panel", "6panel"),
  cell_count = 1000,
  palette = NULL,
  base_size = 11
)

Arguments

benchmark_data

Benchmark summary table or named list containing Category VIII metrics.

type

Character. Visualization type: "composite" (multi-panel dashboard), "runtime" (elapsed wall-clock time barplot), "memory" (peak RAM barplot), "tradeoff" (runtime vs memory scatter/line curve), "cost" (combined resource footprint index), "throughput" (cells/sec), or "efficiency" (CPU/elapsed concurrency ratio if CPU time available). Default "composite".

layout

Character. For composite dashboards: "4panel" (runtime, memory, tradeoff, cost footprint) or "6panel" (adds throughput). Default "4panel".

cell_count

Integer. Number of cells processed in the benchmark, used to calculate simulation throughput (cells / second). Default 1000.

palette

Optional named character vector of simulator colors.

base_size

Numeric. Base font size. Default 11.

Value

A ggplot or patchwork object.

Details

Evaluates computational resource efficiency across single-cell simulators using raw benchmarking measurements (elapsed real wall-clock time in seconds and peak resident memory consumption in MiB).

Computational Scalability and Resource Footprint

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.