ProteinDataPrep

Description

ProteinDataPrep

ProteinDataPrep

Details

Handles data preparation for differential expression analysis: contaminant/decoy filtering, peptide-to-protein aggregation, and normalization.

Public fields

prolfq_app_config
ProlfquAppConfig
lfq_data_peptide
LFQData peptide level
lfq_data
LFQData protein level (after aggregation)
lfq_data_transformed
normalized LFQData
lfq_data_peptide_transformed
transformed peptide-level LFQData (for nested facades)
aggregator
aggregator object
rowAnnot
ProteinAnnotation
summary
data.frame with contaminant/decoy summary

Methods

Public methods

Method new()

Initialize ProteinDataPrep

Usage 
ProteinDataPrep\$new(lfq_data_peptide, rowAnnot, prolfq_app_config)
Arguments
lfq_data_peptide
LFQData object at peptide level
rowAnnot
ProteinAnnotation object
prolfq_app_config
ProlfquAppConfig object

Method cont_decoy_summary()

Contaminant + decoy QC summary. Contaminants are kept and only counted here (labelled downstream via the annotation ‘CON’ flag); decoys are kept in the quant data (dropped only at the model fit) and their proportion is reported as an empirical-FDR signal. Neither is removed from the quant.

Usage 
ProteinDataPrep\$cont_decoy_summary()

Method aggregate()

Aggregate peptide data to protein level

Usage 
ProteinDataPrep\$aggregate()

Method get_aggregation_plots()

Get aggregation plots

Usage 
ProteinDataPrep\$get_aggregation_plots(exp_nr_children = 2)
Arguments
exp_nr_children
minimum number of peptides per protein; default 2

Method write_aggregation_plots()

Write aggregation plots to file

Usage 
ProteinDataPrep\$write_aggregation_plots(exp_nr_children = 2)
Arguments
exp_nr_children
minimum number of peptides per protein; default 2

Method transform_data()

Transform and normalize protein-level data

Usage 
ProteinDataPrep\$transform_data()

Method transform_peptide_data()

Transform peptide-level data (for nested facades like lmer/ropeca)

Usage 
ProteinDataPrep\$transform_peptide_data()

Method build_deanalyse()

Build a DEAnalyse object with the correct data for the chosen facade

Usage 
ProteinDataPrep\$build_deanalyse(contrasts, default_model = NULL)
Arguments
contrasts
named character vector of contrast definitions
default_model
facade registry key, or NULL to read from config
Returns

DEAnalyse R6 object

Method clone()

The objects of this class are cloneable with this method.

Usage 
ProteinDataPrep\$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.

Examples

library("prolfquapp")

pep <- prolfqua::sim_lfq_data_peptide_config(Nprot = 100)
pep <- prolfqua::LFQData$new(pep$data, pep$config)
pA <- data.frame(protein_Id = unique(pep$data_long()$protein_Id))
pA <- pA |> dplyr::mutate(fasta.annot = paste0(pA$protein_Id, "_description"))
pA <- prolfquapp::ProteinAnnotation$new(pep, row_annot = pA, description = "fasta.annot")
GRP2 <- prolfquapp::make_DEA_config_R6()
GRP2$processing_options$transform <- "robscale"

data_prep <- prolfquapp::ProteinDataPrep$new(pep, pA, GRP2)
data_prep$cont_decoy_summary()
  totalNrOfProteins percentOfContaminants percentOfDecoys
1               100                     0               0
data_prep$aggregate()
data_prep$transform_data()