ContrastsProDA | R Documentation |
ContrastsProDA Wrapper to results produced by proDA
ContrastsProDA Wrapper to results produced by proDA
prolfqua::ContrastsInterface
-> ContrastsProDA
contrast_result
contrast result
modelName
model name
subject_Id
columns with protein ID's
contrasts
named vector of length 1.
new()
initialize
ContrastsProDA$new( contrastsdf, contrasts, subject_Id = "name", modelName = "ContrastProDA" )
contrastsdf
data.frame returned by proDA
contrasts
contrasts
subject_Id
column name with protein ID's
modelName
name of model default value ContrastProDA
get_contrast_sides()
show names of contrasts
ContrastsProDA$get_contrast_sides()
data.frame
get_linfct()
get linear function used to determine contrasts
ContrastsProDA$get_linfct()
data.frame
get_contrasts()
get contrasts
ContrastsProDA$get_contrasts(all = FALSE)
all
(default FALSE)
get_Plotter()
get Contrast_Plotter
ContrastsProDA$get_Plotter( fcthreshold = 1, fdrthreshold = 0.1, tstatthreshold = 5 )
fcthreshold
fold change threshold to show
fdrthreshold
FDR threshold
tstatthreshold
t statistics threshold
to_wide()
convert to wide format
ContrastsProDA$to_wide(columns = c("t_statistic", "adj_pval"))
columns
value column default t_statistic, adj_pval
write()
write results
ContrastsProDA$write(path, filename, format = "xlsx")
path
directory
filename
file to write to
format
default xlsx lfq_write_table
clone()
The objects of this class are cloneable with this method.
ContrastsProDA$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other modelling:
Contrasts
,
ContrastsMissing
,
ContrastsModerated
,
ContrastsPlotter
,
ContrastsROPECA
,
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 <- prolfqua::sim_lfq_data_peptide_config()
istar$config <- istar$config
istar_data <- istar$data
lfd <- LFQData$new(istar_data, istar$config)
se <- prolfqua::LFQDataToSummarizedExperiment(lfd)
if(require(proDA)){
fit <- proDA::proDA(se, design = ~ group_ - 1, data_is_log_transformed = TRUE)
contr <- list()
contrasts <- c("group_AvsCtrl" = "group_A - group_Ctrl",
"group_BvsCtrl" = "group_B - group_Ctrl")
contr[["group_AvsCtrl"]] <- data.frame(
contrast = "group_AvsCtrl",
proDA::test_diff(fit, contrast = "group_A - group_Ctrl"))
contr[["group_BvsCtrl"]] <- data.frame(
contrast = "group_BvsCtrl",
proDA::test_diff(fit, contrast = "group_B - group_Ctrl"))
bb <- dplyr::bind_rows(contr)
cproDA <- ContrastsProDA$new(bb, contrasts = contrasts, subject_Id = "name")
x <- cproDA$get_contrasts()
cproDA$get_linfct()
contsides <- cproDA$get_contrast_sides()
stopifnot(ncol(cproDA$to_wide()) == c(7))
tmp <- cproDA$get_Plotter()
tmp$volcano()$pval
tmp$volcano()$adj_pval
}
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