Aggregates top N intensities

run rank_peptide_by_intensity first

Usage

aggregate_intensity_topN(pdata, config, .func, N = 3)

Arguments

pdata

data.frame

config

AnalysisConfiguration

N

default 3 top intensities.

func

function to use for aggregation

Value

list with data and new reduced configuration (config)

See also

Other aggregation: INTERNAL_FUNCTIONS_BY_FAMILY, estimate_intensity(), intensity_summary_by_hkeys(), medpolish_estimate(), medpolish_estimate_df(), medpolish_estimate_dfconfig(), medpolish_protein_estimates(), plot_estimate(), plot_hierarchies_add_quantline(), plot_hierarchies_line(), plot_hierarchies_line_df(), rlm_estimate(), rlm_estimate_dfconfig()

Examples

dd <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
config <- dd$config
res <- dd$data
ranked <- rank_peptide_by_intensity(res, config)
#> Joining with `by = join_by(protein_Id, peptide_Id)`
#> Columns added : srm_meanInt srm_meanIntRank

mean_f <- function(x, name = FALSE) {
  if (name) {
    return("mean")
  }
  mean(x, na.rm = TRUE)
}
sum_f <- function(x, name = FALSE) {
  if (name) {
    return("sum")
  }
  sum(x, na.rm = TRUE)
}

resTOPN <- aggregate_intensity_topN(
  ranked,
  config,
  .func = mean_f,
  N = 3
)

print(dim(resTOPN$data))
#> [1] 116   8
# stopifnot(dim(resTOPN$data) == c(3260, 8))
stopifnot(names(resTOPN) %in% c("data", "config"))
config$get_response()
#> [1] "abundance"
tmpRob <- plot_estimate(ranked,
  config,
  resTOPN$data,
  resTOPN$config,
  show.legend = TRUE
)
stopifnot("ggplot" %in% class(tmpRob$plots[[4]]))