R/tidyMS_R6_TransitionCorrelations.R
get_robscales.Rdcompute median and standard deviation for each sample
get_robscales(data, config)Other preprocessing:
INTERNAL_FUNCTIONS_BY_FAMILY,
apply_to_response_matrix(),
filter_proteins_by_peptide_count(),
normalize_log2_robscale(),
robust_scale(),
scale_with_subset(),
scale_with_subset_by_factors()
bb <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from fileName column
#> Warning: no nr_children column specified in the data, adding column nr_children and setting to 1.
#> completing cases
conf <- bb$config
sample_analysis <- bb$data
pepIntensityNormalized <- transform_work_intensity(sample_analysis, conf, log2)
#> Column added : log2_abundance
s1 <- get_robscales(pepIntensityNormalized, conf)
res <- scale_with_subset(pepIntensityNormalized, pepIntensityNormalized, conf)
#> Warning: Expected 2 pieces. Additional pieces discarded in 336 rows [1, 2, 3, 4, 5, 6,
#> 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
#> Joining with `by = join_by(sampleName, protein_Id, peptide_Id)`
s2 <- get_robscales(res$data, conf)
abs(mean(s1$mads) - mean(s2$mads)) < 0.1
#> [1] TRUE