Compute OpDEA ranking across multiple methods

For each metric, aggregates across comparisons (mean and median), then ranks methods in descending order (rank 1 = best). Final rank = average of individual metric ranks.

Usage

bm_compute_ranking(opdea_combined, metrics = .BM_METRICS)

Arguments

opdea_combined

data.frame with columns: Assay, Comparison, nMCC, G_mean, pAUC_001, pAUC_005, pAUC_010

metrics

Character vector of metrics to include in ranking (default: all 5 OpDEA metrics)

Value

Named list with:

mean_aggregated

data.frame of mean metric values per Assay

median_aggregated

data.frame of median metric values per Assay

mean_ranking

data.frame of ranks based on mean aggregation

median_ranking

data.frame of ranks based on median aggregation

opdea_combined

Input combined data.frame

n_methods

Number of methods

n_comparisons

Number of comparisons

metrics_used

Metrics included in ranking

Examples

data(nadia_dia)
sp <- utils::read.delim(system.file("extdata", "nadia_dia_spikein.tsv.gz",
                                    package = "NADIA"))
ev <- data.frame(Comparison = rep(c("B-A", "D-A"), each = 2),
                 Species = c("ECOLI", "YEAST"),
                 expected_logFC = c(1, -0.58, 2, -3.3))
opdea_all <- do.call(rbind, lapply(c("cycloess", "quantile"), function(nm) {
  de <- process_proteomics(nadia_dia, norm_method = nm,
                           verbose = FALSE)$DEPs_results
  de$Species <- sp$PG.OrganismId[match(de$Protein.IDs, sp$PG.ProteinGroups)]
  cbind(compute_opdea_metrics(de, ev), Assay = nm)
}))
ranking <- bm_compute_ranking(opdea_all)
ranking$mean_ranking
#>      Assay rank_nMCC rank_G_mean rank_pAUC_001 rank_pAUC_005 rank_pAUC_010
#> 1 cycloess         1           1             1             1             1
#> 2 quantile         2           2             2             2             2
#>   rank_final
#> 1          1
#> 2          2