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Rank normalization methods by MDS1 variance explained

Source: R/Normalization_Metrics.R
nm_rank_mds1.Rd

For each assay in se, computes the percentage of variance captured by the first MDS dimension (using only positive eigenvalues from classical MDS) and returns a ranking ordered by ascending MDS1 variance (lower = better).

Usage

nm_rank_mds1(
  se,
  assay_names = NULL,
  condition_col = "Condition",
  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.

verbose

Logical. Print progress messages. Default TRUE.

Value

A data.frame with columns Method, MDS1_VarPct, Rank, ordered by MDS1_VarPct ascending (lower = better; rank 1 = best).

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_mds1(se, verbose = FALSE)
#>     Method MDS1_VarPct Rank
#> 1 cycloess    27.18069    1
#> 2      MAD    59.70161    2
#> 3     log2    62.30919    3
#> 4 quantile    63.26223    4