ProteinDataPrep

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

• ProteinDataPrep$new()

• ProteinDataPrep$cont_decoy_summary()

• ProteinDataPrep$remove_cont_decoy()

• ProteinDataPrep$aggregate()

• ProteinDataPrep$get_aggregation_plots()

• ProteinDataPrep$write_aggregation_plots()

• ProteinDataPrep$transform_data()

• ProteinDataPrep$transform_peptide_data()

• ProteinDataPrep$build_deanalyse()

• ProteinDataPrep$clone()

---

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()

Count number of contaminants and decoys

Usage

ProteinDataPrep$cont_decoy_summary()

---

Method remove_cont_decoy()

Remove contaminants and decoys from peptide data

Usage

ProteinDataPrep$remove_cont_decoy()

---

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 = "lm_missing")

Arguments

contrasts

named character vector of contrast definitions

default_model

facade registry key (default "lm_missing")

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

pep <- prolfqua::sim_lfq_data_peptide_config(Nprot = 100)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
pep <- prolfqua::LFQData$new(pep$data, pep$config)
pA <- data.frame(protein_Id = unique(pep$data$protein_Id))
pA <- pA |> dplyr::mutate(fasta.annot = paste0(pA$protein_Id, "_description"))
pA <- prolfquapp::ProteinAnnotation$new(pep, row_annot = pA, description = "fasta.annot")
#> Warning: no exp_nr_children column specified, computing using nr_obs_experiment function
GRP2 <- prolfquapp::make_DEA_config_R6()
GRP2$processing_options$transform <- "robscale"

data_prep <- prolfquapp::ProteinDataPrep$new(pep, pA, GRP2)
data_prep$cont_decoy_summary()
#>   totalNrOfProteins percentOfContaminants percentOfFalsePositives
#> 1               100                     0                       0
#>   NrOfProteinsNoDecoys
#> 1                  100
data_prep$remove_cont_decoy()
#> Joining with `by = join_by(protein_Id)`
#> INFO [2026-03-23 19:48:08] removing contaminants and reverse sequences with patterns: ^zz|^CON|Cont_^REV_|^rev_
data_prep$aggregate()
#> INFO [2026-03-23 19:48:08] AGGREGATING PEPTIDE DATA: medpolish.
#> Column added : log_abundance
#> starting aggregation
#> Column added : exp_medpolish
#> INFO [2026-03-23 19:48:09] END OF PROTEIN AGGREGATION
data_prep$transform_data()
#> INFO [2026-03-23 19:48:09] Transforming using robscale.
#> Column added : log2_exp_medpolish
#> data is : TRUE
#> Joining with `by = join_by(protein_Id, sampleName, isotopeLabel)`
#> INFO [2026-03-23 19:48:09] Transforming data : robscale.