Compute a combined final ranking across PCV, PMAD, PEV, Correlation and group-separation (PC1 F-ratio)

For each method, the final rank is the mean of five individual ranks: four intragroup-precision metrics (pcv, pmad, pev, cor) plus a group-separation metric (Rank_Sep, the ANOVA F-ratio on PC1 scores, higher = better separation). Lower final rank = better overall normalization quality.

Usage

nm_rank_final(
  se,
  assay_names = NULL,
  condition_col = "Condition",
  cor_method = "pearson",
  verbose = TRUE
)

Arguments

se

SummarizedExperiment from import_norm_matrices().

assay_names

Character vector of assay names to include. NULL = all.

condition_col

Column in colData with condition labels.

cor_method

Correlation method forwarded to nm_rank_cor().

verbose

Logical. Print progress messages. Default TRUE.

Value

A data.frame with columns: Method, Rank_PCV, Rank_PMAD, Rank_PEV, Rank_Cor, Rank_Sep, Rank_Final, ordered by Rank_Final ascending (best first).

Details

Note: earlier versions averaged Rank_PC1 (PC1 variance %, higher = better) and Rank_MDS1 (MDS1 variance %, lower = better). Both capture the same dominant axis of variation but with opposite directions, so they partially cancelled within the mean. They are replaced here by a single, unambiguous group-separation rank based on the PC1 F-ratio. The standalone nm_rank_pc1() / nm_rank_mds1() functions are unchanged for individual inspection.

Examples

data(nadia_dia)
se <- nm_prepare_se(nadia_dia, verbose = FALSE)
se <- nm_run_normalizations(se, methods = c("cycloess", "quantile", "MAD"),
                            verbose = FALSE)

nm_rank_final(se, verbose = FALSE)
#>     Method Rank_PCV Rank_PMAD Rank_PEV Rank_Cor Rank_Sep Rank_Final
#> 1 cycloess        1         1        1        1        3        1.4
#> 2 quantile        3         2        2        4        1        2.4
#> 3      MAD        2         3        3        3        2        2.6
#> 4     log2        4         4        4        2        4        3.6