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Build a SummarizedExperiment for imputation benchmarking

Source: R/Imputation_Metrics.R
im_prepare_se.Rd

Convenience function that creates a SE from a proteomics_data object and applies the winning normalization method (from normalization benchmarking). The result is ready for imputation_metrics().

Usage

im_prepare_se(
  preprocessing,
  norm_method = NULL,
  pc1_rank = NULL,
  min_reps = NULL,
  min_groups = 1,
  covariate_df = NULL,
  norm_method_args = list(),
  include_log2 = TRUE,
  verbose = TRUE
)

Arguments

preprocessing

A proteomics_data object, from any of the preprocess_*() functions.

norm_method

Character scalar. Normalization method name (e.g. "cycloess"). If provided, used directly. Default NULL.

pc1_rank

data.frame from nm_rank_pc1() or normalization_metrics()$pc1_rank. The first row's Method column is used as winner. Ignored if norm_method is provided. Default NULL.

min_reps

Minimum replicates with non-NA values per group for protein filtering. If NULL, auto-computed as half the smallest group. Default NULL.

min_groups

Minimum groups meeting min_reps (default: 1).

covariate_df

Optional covariate data.frame for paired designs (must contain a Column column). Default NULL.

norm_method_args

Named list of per-method arguments for normalization. Default list().

include_log2

Logical. Include the baseline "log2" assay in the returned SE. Default TRUE.

verbose

Logical. Print progress messages. Default TRUE.

Value

SummarizedExperiment with assays "log2" (optional) + winner method.

Details

Internally calls nm_prepare_se() to build the baseline SE with assay "log2", then applies the chosen normalization via .nm_dispatch_normalization().

Examples

data(nadia_dia)

# Option A: name the normalization to benchmark imputation on
se <- im_prepare_se(nadia_dia, norm_method = "cycloess", verbose = FALSE)
SummarizedExperiment::assayNames(se)
#> [1] "log2"     "cycloess"

# Option B: let the winner of normalization_metrics() decide. That function
# takes a SummarizedExperiment, so the baseline is built first.
se_base <- nm_prepare_se(nadia_dia, verbose = FALSE)
nm <- normalization_metrics(se_base,
                            methods = c("cycloess", "quantile", "MAD"),
                            plots = "pc1_ranking", verbose = FALSE)
nm$pc1_rank
#>     Method PC1_VarPct Rank
#> 1 cycloess   72.57836    1
#> 2 quantile   65.48778    2
#> 3      MAD   64.86712    3
#> 4     log2   56.03859    4
se2 <- im_prepare_se(nadia_dia, pc1_rank = nm$pc1_rank, verbose = FALSE)
SummarizedExperiment::assayNames(se2)
#> [1] "log2"     "cycloess"