Limma moderated contrasts

Limma moderated contrasts

Limma moderated contrasts

Value

An R6 class generator.

See also

moderated_p_limma

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, ContrastsRfitImputeFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, StrategyRfit, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_glm_protein(), build_model_impute(), build_model_limma(), build_model_limma_impute(), build_model_limma_voom(), build_model_limma_voom_impute(), build_model_limpa(), build_model_logistf(), compute_borrowed_variance(), compute_borrowed_variance_limma(), compute_contrast(), compute_lmer_contrast(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_from_model(), impute_refit_singular(), linfct_from_model(), linfct_matrix_contrasts(), list_facades(), lookup_facade(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), new_imputed_model(), pivot_model_contrasts_to_wide(), register_facade(), sigma.rfit_prolfqua(), sim_build_models_lm(), sim_build_models_lmer(), sim_build_models_logistf(), sim_make_model_lm(), sim_make_model_lmer(), strategy_limma(), strategy_limpa(), strategy_logistf(), summary_ROPECA_median_p_scaled(), vcov.rfit_prolfqua()

Super class

prolfqua::ContrastsInterface -> ContrastsModerated

Public fields

Contrast

Class implementing the Contrast interface

model_name

name of model

subject_id

columns with subject_id (proteinID)

p.adjust

function to adjust p-values

variance_floor

optional lower bound for posterior variances

Methods

Public methods

• ContrastsModerated$new()

• ContrastsModerated$get_contrast_sides()

• ContrastsModerated$get_linfct()

• ContrastsModerated$get_contrasts()

• ContrastsModerated$clone()

Inherited methods

• prolfqua::ContrastsInterface$column_description()
• prolfqua::ContrastsInterface$contrast_summary_table()
• prolfqua::ContrastsInterface$extra_artifacts()
• prolfqua::ContrastsInterface$filter_significant()
• prolfqua::ContrastsInterface$get_Plotter()
• prolfqua::ContrastsInterface$get_config()
• prolfqua::ContrastsInterface$get_missing()
• prolfqua::ContrastsInterface$get_ora()
• prolfqua::ContrastsInterface$get_rank()
• prolfqua::ContrastsInterface$to_wide()

---

Method new()

initialize

Usage

ContrastsModerated$new(
  Contrast,
  model_name = paste0(Contrast$model_name, "_moderated"),
  p.adjust = prolfqua::adjust_p_values,
  variance_floor = NULL
)

Arguments

Contrast

class implementing the ContrastInterface

model_name

name of the model

p.adjust

function to adjust p-values - default BH

variance_floor

optional positive lower bound for posterior variances

---

Method get_contrast_sides()

get both sides of contrasts

Usage

ContrastsModerated$get_contrast_sides()

---

Method get_linfct()

get linear functions from contrasts

Usage

ContrastsModerated$get_linfct(global = TRUE)

Arguments

global

logical TRUE - get the a linear functions for all models, FALSE - linear function for each model

---

Method get_contrasts()

applies limma moderation

Usage

ContrastsModerated$get_contrasts(all = FALSE)

Arguments

all

should all columns be returned (default FALSE)

global

use a global linear function (determined by get_linfct)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsModerated$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_protein_config(Nprot = 50)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
protIntensity <- istar$data
config <- istar$config


lProt <- LFQData$new(protIntensity, config)
lProt$rename_response("transformedIntensity")
modelFunction <-
  strategy_lm("transformedIntensity  ~ group_")
mod <- build_model(
 lProt,
 modelFunction)

Contr <- c("dil.b_vs_a" = "group_A - group_Ctrl")
contrast <- prolfqua::Contrasts$new(mod,
 Contr)
contrast <- ContrastsModerated$new(contrast)
bb <- contrast$get_contrasts()
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
# LOD-imputed refit for proteins the plain lm cannot estimate
imp <- ContrastsLMImputeFacade$new(lProt, "~ group_", Contr)$contrast
#> Warning: essentially perfect fit: summary may be unreliable

contrast$get_contrasts() |> dim()
#> [1] 49 16
(xx <- imp$get_contrasts())   |> dim()
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> [1] 50 16
merged <- merge_contrasts_results(contrast, imp)
#> Joining with `by = join_by(protein_Id, contrast)`
#> Joining with `by = join_by(protein_Id, contrast)`

merged$more$get_contrasts() |> dim()
#> [1]  1 16
stopifnot(all(dim(merged$merged$get_contrasts()) == c(50, 16)))
stopifnot(all(dim(merged$same$get_contrasts()) == c(49, 16)))

cs <- contrast$get_contrast_sides()
cslf <- contrast$get_linfct()
ctr <- contrast$get_contrasts()
ctrwide <- contrast$to_wide()
cp <- contrast$get_Plotter()

print(cp$histogram()$p.value, vp=NULL)

print(cp$histogram()$FDR, vp = NULL)


cp$volcano()
#> $FDR

#> 
cp$ma_plot()