rlm_estimate: Estimate e.g. protein abundance from peptides using MASS:rlm

In wolski/prolfqua: Proteomics Label Free Quantification

Estimate e.g. protein abundance from peptides using MASS:rlm

Description

Estimate e.g. protein abundance from peptides using MASS:rlm

Usage

rlm_estimate(pdata, response, feature, samples, maxIt = 20)

Arguments

pdata	data
response	intensities
feature	e.g. peptideIDs.
sample	e.g. sampleName

See Also

Other aggregation: INTERNAL_FUNCTIONS_BY_FAMILY, aggregate_intensity_topN(), 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_dfconfig()

Examples

xx <- data.frame(response = rnorm(20,0,10), feature = rep(LETTERS[1:5],4), samples= rep(letters[1:4],5))

bb <- rlm_estimate(xx , "response", "feature", "samples", maxIt = 20)

xx2 <- data.frame(log2Area = rnorm(20,0,10), peptide_Id = rep(LETTERS[1:5],4), sampleName = rep(letters[1:4],5))
rlm_estimate(xx2, "log2Area", "peptide_Id", "sampleName")
rlm_estimate(prolfqua_data('data_checksummarizationrobust87'),"log2Area", "peptide_Id", "sampleName")
rlm_estimate(prolfqua_data('data_checksummarizerobust69'),"log2Area", "peptide_Id", "sampleName")
res <- vector(100,mode = "list")
for (i in seq_len(100)) {
  xx3 <- xx2
  xx3$log2Area[sample(1:20,sample(1:15,1))] <- NA
  res[[i]] <- list(data = xx3, summary = rlm_estimate(xx3, "log2Area", "peptide_Id", "sampleName"))
}
rlm_estimate(xx2[xx2$peptide_Id == 'A',],"log2Area", "peptide_Id", "sampleName")
rlm_estimate(xx2[xx2$sampleName == 'a',],"log2Area", "peptide_Id", "sampleName")


bb <- prolfqua_data('data_ionstar')$filtered()
bb$config <- old2new(bb$config)
stopifnot(nrow(bb$data) == 25780)
conf <- bb$config
data <- bb$data
conf$table$hierarchyDepth = 1
xnested <- data |>
  dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()

feature <- base::setdiff(conf$table$hierarchy_keys(),  conf$table$hierarchy_keys_depth())
x <- xnested$data[[1]]
bb <- rlm_estimate(x,
 response = conf$table$get_response(),
  feature = feature,
   samples = conf$table$sampleName)

wolski/prolfqua documentation built on May 12, 2024, 10:16 p.m.