Summarize peptide quantity to protein quantity

Aggregation peptide to protein vis 'aggrefun'.

Usage

aggregate_pe(
  pe,
  aggrefun = c("RobustSummary", "medianPolish", "totalMean"),
  aggregate_Peptide_Type = c("Unique + Razor", "Unique"),
  fcol,
  i = "peptideNorm",
  reserve = "Gene.names"
)

Arguments

pe

A QFeatures object, contains the normalized peptide assay

aggrefun

A function used for quantitative feature aggregation. It can be a character in "RobustSummary","medianPolish","totalMean" or other function. Details see aggregateFeatures

aggregate_Peptide_Type

Character in "Unique + Razor" or "Unique". Use what kind of peptides to summarise proteins. If choose "Unique", return output just save unique peptides in smallest proteingroups.

fcol

Character(1), defining how to summarise the features. Exist in rowData(pe).

i

Character(1), name of the assay to be aggregated.

reserve

Character, the column(s) which will reserve after aggregate, such as the columns store protein information can.

Value

A QFeatures object with a new protein aggregation assay.

Examples

if (FALSE) {
# Load example peptide data
data(Silicosis_peptide)
ecols <- grep("Intensity.", colnames(Silicosis_peptide), value = TRUE)

# construct QFeatures object
pe_peptides <- make_pe_parse(Silicosis_peptide, columns = ecols, remove_prefix = TRUE, log2transform = TRUE,mode = "delim")

# Filter, imputation
pe <- filter_pe(pe_peptides, thr = 1,fraction = 0.4, filter_formula = ~ Reverse != '+' & Potential.contaminant !="+" )
pe <- impute_pe(pe, fun = "QRILC", name = "peptideImp")

# Normalization
pe <- normalize_pe(pe,method = "quantiles", i = "peptideImp", name = "peptideNorm")

# Summarize peptide value to protein quantity
protein_pe <- DEP2::aggregate_pe(pe, fcol = "Proteins", i = "peptideNorm")
class(protein_pe)

}