aggregate_contrast: Compute fold changes given Contrasts

In wolski/prolfqua: Proteomics Label Free Quantification

Compute fold changes given Contrasts

Description

Compute fold changes given Contrasts

Usage

aggregate_contrast(
  data,
  subject_Id,
  agg_func = list(median = function(x) {
     stats::median(x, na.rm = TRUE)
 }, mad =
    function(x) {
     stats::mad(x, na.rm = TRUE)
 }),
  contrast = "contrast"
)

See Also

Other imputation: UpSet_interaction_missing_stats(), UpSet_missing_stats(), get_contrast(), missigness_histogram(), missigness_impute_factors_interactions(), missingness_per_condition(), missingness_per_condition_cumsum()

Examples

istar <- sim_lfq_data_peptide_config()
config <- istar$config
analysis <- istar$data

Contrasts <- c("dilution.b-a" = "group_A - group_B", "dilution.c-e" = "group_A - group_Ctrl")
mean <- missigness_impute_factors_interactions(analysis, config, value = "meanAbundance" )
mean <- get_contrast(mean, config$table$hierarchy_keys(), Contrasts)
meanProt <- aggregate_contrast(mean,  subject_Id =  config$table$hierarchy_keys_depth())

imputed <- missigness_impute_factors_interactions(analysis, config, value = "imputed" )
imputed <- get_contrast(imputed, config$table$hierarchy_keys(), Contrasts)

imputedProt <- aggregate_contrast(imputed,  subject_Id =  config$table$hierarchy_keys_depth())
## Not run: 
plot(imputedProt$group_1 - imputedProt$group_2, imputedProt$estimate_median)
abline(c(0,1), col=2, pch = "*")
plot(meanProt$estimate_median - imputedProt$estimate_median )

## End(Not run)
stopifnot(sum(is.na(meanProt$estimate_median)) == 0)
stopifnot(sum(is.na(imputedProt$estimate_median)) == 0)

wolski/prolfqua documentation built on April 27, 2024, 4:09 p.m.