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Scale using a subset of the data, within factor levels (e.g. use for pulldown data)

Source: R/tidyMS_R6_TransitionCorrelations.R
scale_with_subset_by_factors.Rd

This method reduces the variance within the group.

Usage

scale_with_subset_by_factors(data, subset, config, preserveMean = FALSE)

Arguments

data

tibble with data

subset

tibble with subset of the data which will be used to derive scaling parameters

config

configuration

preserveMean

default FALSE then set mean to 0

See also

Other preprocessing: INTERNAL_FUNCTIONS_BY_FAMILY, apply_to_response_matrix(), filter_proteins_by_peptide_count(), get_robscales(), normalize_log2_robscale(), robust_scale(), scale_with_subset()

Examples



bb <- sim_lfq_data_peptide_config(Nprot = 100)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
conf <- bb$config$clone(deep=TRUE)
sample_analysis <- bb$data

res <- transform_work_intensity(sample_analysis, conf, log2)
#> Column added : log2_abundance
res <- scale_with_subset_by_factors(res, res, conf)
#> Joining with `by = join_by(sampleName, isotopeLabel, protein_Id, peptide_Id)`
#> Joining with `by = join_by(sampleName, isotopeLabel, protein_Id, peptide_Id)`
#> Joining with `by = join_by(sampleName, isotopeLabel, protein_Id, peptide_Id)`