ContrastsROPECA | R Documentation |
ROPECA reproducibility-optimization method
ROPECA reproducibility-optimization method
ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression.
prolfqua::ContrastsInterface
-> ContrastsROPECA
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
new()
initialize
ContrastsROPECA$new( Contrast, modelName = "ROPECA", p.adjust = prolfqua::adjust_p_values )
Contrast
e.g. instance of Contrasts class, or ContrastsModerated
modelName
default ROPECA
p.adjust
function to use for p.value adjustement
get_contrast_sides()
show names of contrasts
ContrastsROPECA$get_contrast_sides()
data.frame
get_linfct()
get linear function used to determine contrasts
ContrastsROPECA$get_linfct()
data.frame
get_contrasts()
get contrasts
ContrastsROPECA$get_contrasts(all = FALSE)
all
should all columns be returned (default FALSE)
global
use a global linear function (determined by get_linfct)
data.frame
get_Plotter()
get ContrastsPlotter
ContrastsROPECA$get_Plotter(FDRthreshold = 0.1, FCthreshold = 2)
FDRthreshold
FDR threshold
FCthreshold
FC threshold
ContrastsPlotter
to_wide()
convert to wide format
ContrastsROPECA$to_wide( columns = c("beta.based.significance", "FDR.beta.based.significance") )
columns
value column default beta.based.significance
data.frame
clone()
The objects of this class are cloneable with this method.
ContrastsROPECA$clone(deep = FALSE)
deep
Whether to make a deep clone.
summary_ROPECA_median_p.scaled
Other modelling:
Contrasts
,
ContrastsMissing
,
ContrastsModerated
,
ContrastsPlotter
,
ContrastsProDA
,
ContrastsTable
,
INTERNAL_FUNCTIONS_BY_FAMILY
,
LR_test()
,
Model
,
build_model()
,
contrasts_fisher_exact()
,
get_anova_df()
,
get_complete_model_fit()
,
get_p_values_pbeta()
,
isSingular_lm()
,
linfct_all_possible_contrasts()
,
linfct_factors_contrasts()
,
linfct_from_model()
,
linfct_matrix_contrasts()
,
merge_contrasts_results()
,
model_analyse()
,
model_summary()
,
moderated_p_limma()
,
moderated_p_limma_long()
,
my_contest()
,
my_contrast()
,
my_contrast_V1()
,
my_contrast_V2()
,
my_glht()
,
pivot_model_contrasts_2_Wide()
,
plot_lmer_peptide_predictions()
,
sim_build_models_lm()
,
sim_build_models_lmer()
,
sim_make_model_lm()
,
sim_make_model_lmer()
,
strategy_lmer()
,
summary_ROPECA_median_p.scaled()
istar <- istar <- prolfqua::sim_lfq_data_peptide_config(Nprot=50)
istar$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()
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