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Differential expression analysis engine using prolfqua facade classes

Source: R/R6_DEAnalyse.R
DEAnalyse.Rd

Differential expression analysis engine using prolfqua facade classes

Differential expression analysis engine using prolfqua facade classes

Details

Takes prepared LFQData (at the correct hierarchy level for the chosen facade) and runs statistical modelling via prolfqua's ContrastsFacade classes.

The caller (e.g. ProteinDataPrep$build_deanalyse()) is responsible for providing data at the right level: aggregated protein-level for most facades, or nested peptide-level for lmer/ropeca.

Public fields

prolfq_app_config

ProlfquAppConfig

lfq_data

LFQData to model (transformed, at correct hierarchy level)

lfq_data_raw

raw (untransformed) LFQData for reporting

rowAnnot

ProteinAnnotation

contrasts

vector with contrasts

FDR_threshold

FDR threshold

diff_threshold

difference threshold

summary

data.frame with contaminant/decoy summary

annotated_contrasts

contrasts joined with row annotations

annotated_contrasts_signif

significant annotated contrasts

formula

model formula

contrast_results

named list of facade objects

default_model

facade registry key for the default model

Methods

Public methods

Method new()

Initialize DEAnalyse

Usage

DEAnalyse$new(
  lfq_data,
  rowAnnot,
  prolfq_app_config,
  contrasts,
  default_model = "lm_missing",
  lfq_data_raw = NULL,
  summary = NULL
)

Arguments

lfq_data

LFQData to model (transformed, at correct hierarchy level)

rowAnnot

ProteinAnnotation object

prolfq_app_config

ProlfquAppConfig object

contrasts

named vector of contrast definitions

default_model

facade registry key (default "lm_missing")

lfq_data_raw

raw (untransformed) LFQData for reporting (optional)

summary

data.frame with contaminant/decoy summary (optional)

Method build_facade()

Build a facade by registry key

Usage

DEAnalyse$build_facade(name, modelstr = NULL)

Arguments

name

facade registry key (e.g. "lm", "lm_missing", "limma")

modelstr

model formula string; auto-generated if NULL

Returns

the facade object (invisibly)

Method build_default()

Build the default facade (as set in default_model)

Usage

DEAnalyse$build_default()

Method get_annotated_contrasts()

Join default model contrasts with protein row annotations

Usage

DEAnalyse$get_annotated_contrasts()

Method filter_contrasts()

Return contrast rows passing FDR and difference thresholds

Usage

DEAnalyse$filter_contrasts()

Method clone()

The objects of this class are cloneable with this method.

Usage

DEAnalyse$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$diff_threshold <- 0.2
GRP2$processing_options$transform <- "robscale"

contrasts <- c("AVsC" = "group_A - group_Ctrl", BVsC = "group_B - group_Ctrl")

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$remove_cont_decoy()
#> Joining with `by = join_by(protein_Id)`
#> INFO [2026-03-23 19:48:01] removing contaminants and reverse sequences with patterns: ^zz|^CON|Cont_^REV_|^rev_
data_prep$aggregate()
#> INFO [2026-03-23 19:48:01] AGGREGATING PEPTIDE DATA: medpolish.
#> Column added : log_abundance
#> starting aggregation
#> Column added : exp_medpolish
#> INFO [2026-03-23 19:48:03] END OF PROTEIN AGGREGATION
data_prep$transform_data()
#> INFO [2026-03-23 19:48:03] Transforming using robscale.
#> Column added : log2_exp_medpolish
#> data is : TRUE
#> Joining with `by = join_by(protein_Id, sampleName, isotopeLabel)`
#> INFO [2026-03-23 19:48:03] Transforming data : robscale.

deanalyse <- data_prep$build_deanalyse(contrasts)
deanalyse$build_default()
#> INFO [2026-03-23 19:48:03] model formula: normalized_abundance ~ group_
#> Joining with `by = join_by(protein_Id)`
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> completing cases
#> AVsC=group_A - group_Ctrl
#> BVsC=group_B - group_Ctrl
#> AVsC=group_A - group_Ctrl
#> BVsC=group_B - group_Ctrl
#> AVsC=group_A - group_Ctrl
#> BVsC=group_B - group_Ctrl
#> Joining with `by = join_by(protein_Id, contrast)`
#> Joining with `by = join_by(protein_Id, contrast)`
stopifnot(nrow(deanalyse$contrast_results[[deanalyse$default_model]]$get_contrasts()) == 200)