ContrastsROPECA: ROPECA reproducibility-optimization method

In wolski/prolfqua: Proteomics Label Free Quantification

ROPECA reproducibility-optimization method

Description

ROPECA reproducibility-optimization method

ROPECA reproducibility-optimization method

Details

ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression.

Super class

prolfqua::ContrastsInterface -> ContrastsROPECA

Public fields

Contrast

Contrast

contrast_result

contrast result

modelName

model name

subject_Id

columns with protein ID's

p.adjust

method to use for p.value adjustment

Methods

Public methods

• ContrastsROPECA$new()

• ContrastsROPECA$get_contrast_sides()

• ContrastsROPECA$get_linfct()

• ContrastsROPECA$get_contrasts()

• ContrastsROPECA$get_Plotter()

• ContrastsROPECA$to_wide()

• ContrastsROPECA$clone()

Inherited methods

• prolfqua::ContrastsInterface$column_description()

---

Method new()

initialize

Usage

ContrastsROPECA$new(
  Contrast,
  modelName = "ROPECA",
  p.adjust = prolfqua::adjust_p_values
)

Arguments

Contrast

e.g. instance of Contrasts class, or ContrastsModerated

modelName

default ROPECA

p.adjust

function to use for p.value adjustement

---

Method get_contrast_sides()

show names of contrasts

Usage

ContrastsROPECA$get_contrast_sides()

Returns

data.frame

---

Method get_linfct()

get linear function used to determine contrasts

Usage

ContrastsROPECA$get_linfct()

Returns

data.frame

---

Method get_contrasts()

get contrasts

Usage

ContrastsROPECA$get_contrasts(all = FALSE)

Arguments

all

should all columns be returned (default FALSE)

global

use a global linear function (determined by get_linfct)

Returns

data.frame

---

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsROPECA$get_Plotter(FDRthreshold = 0.1, FCthreshold = 2)

Arguments

FDRthreshold

FDR threshold

FCthreshold

FC threshold

Returns

ContrastsPlotter

---

Method to_wide()

convert to wide format

Usage

ContrastsROPECA$to_wide(
  columns = c("beta.based.significance", "FDR.beta.based.significance")
)

Arguments

columns

value column default beta.based.significance

Returns

data.frame

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsROPECA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

summary_ROPECA_median_p.scaled

Other modelling: Contrasts, ContrastsMissing, ContrastsModerated, ContrastsPlotter, ContrastsProDA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, build_model(), build_models(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_imputed_contrasts(), get_p_values_pbeta(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), make_model(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V1(), my_contrast_V2(), my_glht(), pivot_model_contrasts_2_Wide(), plot_lmer_model_and_data(), plot_lmer_peptide_noRandom(), plot_lmer_peptide_predictions(), plot_lmer_predicted_interactions(), strategy_lmer(), summary_ROPECA_median_p.scaled()

Examples

istar <- istar <- prolfqua::sim_lfq_data_peptide_config(Nprot=50)
istar$config <- old2new(istar$config )
istar_data <- istar$data
modelFunction <-
  strategy_lm("abundance  ~ group_")
pepIntensity <- istar_data
config <- istar$config$clone(deep = TRUE)
config$table$hierarchyDepth <- 2
config$table$hierarchy_keys_depth()

mod <- build_model(
 pepIntensity,
 modelFunction,
 subject_Id = config$table$hierarchy_keys_depth())

 Contr <- c("AvsCtrl" = "group_A - group_Ctrl")


 contr <- prolfqua::Contrasts$new(mod, Contr)
 dim(contr$get_contrasts())
 contrM <- prolfqua::ContrastsModerated$new(contr)
 dim(contrM$get_contrasts())
 contrast <- prolfqua::ContrastsROPECA$new(contrM)
 contrast$get_contrasts()
 contrast <- prolfqua::ContrastsROPECA$new(contr)
 tmp <- contrast$get_contrasts()
 dim(tmp)
 pl <- contrast$get_Plotter()
 contrast$to_wide()
 contrast$get_linfct()
 contrast$get_contrast_sides()
 pl$histogram()
 pl$ma_plot()

wolski/prolfqua documentation built on May 2, 2024, 7:23 p.m.