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FiberMargin Benchmarks

Evaluation contract

benchmark_spatial_refiners() evaluates each method on identical inputs and the same reference data:

It reports elapsed time and a standard metric table generated by evaluate_spatial_refinement().

Standard metrics reported

Dataset catalog

available_spatial_benchmarks()
#>   dataset included observations classes scenarios
#> 1   dlpfc     TRUE        47329       7        45
#> 2 merfish    FALSE        28317       8        45
#> 3     crc     TRUE       194541      19        60
#>                                                                  license
#> 1                                Artistic-2.0 (spatialLIBD data package)
#> 2 CC0 raw Dryad data; no explicit license for derived BASS domain labels
#> 3         CC BY 4.0 (10x Genomics source and author-derived annotations)
#>                                                                                               source
#> 1                                                      https://bioconductor.org/packages/spatialLIBD
#> 2                                                              https://doi.org/10.5061/dryad.8t8s248
#> 3 https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc-v4
#>                                                                                                                               note
#> 1                                                             Coordinates, layer labels, and frozen corruption inputs are bundled.
#> 2                                                               Use the publication script with a local processed annotation file.
#> 3 Coordinates, 19 WSI annotation labels, and deterministic corruption recipes are bundled; counts and tissue imagery are excluded.

Load one real scenario

dlpfc <- load_spatial_benchmark("dlpfc", scenario = 1L)
names(dlpfc)
#>  [1] "xy"                     "labels"                 "truth"                 
#>  [4] "samples"                "boundary"               "regions"               
#>  [7] "sparse"                 "name"                   "metadata"              
#> [10] "spot_id"                "subject"                "nearest_adjacent_layer"
#> [13] "scenario"

Example benchmark run

bench <- simulate_spatial_domains(
  n = 5000L,
  pattern = "jagged_stripes",
  noise = 0.20,
  samples = 2L,
  seed = 7L
)

benchmark_spatial_refiners(
  data = bench,
  methods = list(
    FiberMargin = refine_spatial_labels,
    InitialOnly = function(xy, labels, ...) labels
  ),
  include_initial = TRUE,
  seed = 1L
)

Reproducibility scripts

Rscript benchmarks/run_publication_benchmarks.R
Rscript benchmarks/run_fibermargin_publication_benchmarks.R
Rscript benchmarks/run_general_mask_repair_benchmarks.R

Each script stores results and artifacts with fixed random seeds so runs are reproducible.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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