Base class for all Contrasts classes

Base class for all Contrasts classes

Base class for all Contrasts classes

Value

An R6 class generator.

Public fields

config

ContrastConfiguration describing the backend's column-role mapping and behaviour flags. Subclasses should populate this in initialize() so generic consumers can resolve column names without hard-coding them.

Methods

Public methods

• ContrastsInterface$get_config()

• ContrastsInterface$get_contrast_sides()

• ContrastsInterface$get_contrasts()

• ContrastsInterface$get_Plotter()

• ContrastsInterface$to_wide()

• ContrastsInterface$get_missing()

• ContrastsInterface$get_rank()

• ContrastsInterface$get_ora()

• ContrastsInterface$filter_significant()

• ContrastsInterface$contrast_summary_table()

• ContrastsInterface$extra_artifacts()

• ContrastsInterface$column_description()

• ContrastsInterface$clone()

---

Method get_config()

Return the ContrastConfiguration, constructing an LM-flavoured default on demand if a subclass has not set one.

Usage

ContrastsInterface$get_config()

---

Method get_contrast_sides()

get table with sides of the contrast

Usage

ContrastsInterface$get_contrast_sides()

---

Method get_contrasts()

get table with contrast results (similar to limma topTable function)

Usage

ContrastsInterface$get_contrasts()

---

Method get_Plotter()

initialize plotter

Usage

ContrastsInterface$get_Plotter()

---

Method to_wide()

create wide representation of data.

Usage

ContrastsInterface$to_wide()

---

Method get_missing()

get protein × contrast pairs that could not be estimated. Returns a data.frame with hierarchy columns and contrast, or 0 rows.

Usage

ContrastsInterface$get_missing()

---

Method get_rank()

get rank-list input table for enrichment tools.

If the backend's ContrastConfiguration has a p-value column the default rank score is signed sign(effect) * -log10(p.value). Otherwise the default is the effect column itself.

Usage

ContrastsInterface$get_rank(score = NULL)

Arguments

score

column name to use as rank score, or NULL to pick from the config

Returns

data.frame with subject id, contrast, and score columns.

---

Method get_ora()

get ORA input table — features passing the FDR and absolute effect-size threshold in the requested direction.

Usage

ContrastsInterface$get_ora(up = TRUE, FDR_threshold = 0.05, diff_threshold = 1)

Arguments

up

if TRUE return positive effects, otherwise negative effects

FDR_threshold

false discovery rate threshold

diff_threshold

absolute effect-size threshold

Returns

filtered contrast data.frame for ORA input.

---

Method filter_significant()

Filter contrast rows that pass the FDR and effect-size threshold.

By default this is symmetric on the effect column (|effect| > diff_threshold). For backends whose ContrastConfiguration has significance_directional = TRUE (e.g. SAINTexpress, where only positive enrichment is biologically meaningful), the filter is one-sided (effect > diff_threshold).

Usage

ContrastsInterface$filter_significant(FDR_threshold = 0.05, diff_threshold = 1)

Arguments

FDR_threshold

false discovery rate threshold

diff_threshold

effect-size threshold

Returns

filtered contrast data.frame.

---

Method contrast_summary_table()

Per-subject summary table for downstream report grobs. Returns a small data.frame with canonical column names contrast, effect, score, fdr regardless of backend.

Usage

ContrastsInterface$contrast_summary_table(rounded = TRUE)

Arguments

rounded

if TRUE round numeric columns to 3 significant digits (matches the prolfquapp report convention)

---

Method extra_artifacts()

Backend-specific extra artifacts that a report should carry along with the contrast results (e.g. SAINT input tables). Default returns an empty named list.

Usage

ContrastsInterface$extra_artifacts()

---

Method column_description()

column description

Usage

ContrastsInterface$column_description()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsInterface$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

int <- ContrastsInterface$new()
testthat::expect_error(int$get_contrast_sides())
testthat::expect_error(int$get_contrasts())
testthat::expect_error(int$get_missing())
testthat::expect_error(int$get_Plotter())
testthat::expect_error(int$to_wide())
# get_rank / get_ora / filter_significant call get_contrasts() internally,
# which is not implemented on the bare interface, so they surface that error.
testthat::expect_error(int$get_rank())
testthat::expect_error(int$get_ora())
testthat::expect_error(int$filter_significant())
identical(int$extra_artifacts(), list())
#> [1] TRUE
int$column_description()
#>           column_name
#> modelName   modelName
#> contrast     contrast
#> avgAbd         avgAbd
#> diff             diff
#> FDR               FDR
#> statistic   statistic
#> std.error   std.error
#> df                 df
#> p.value       p.value
#> conf.low     conf.low
#> conf.high   conf.high
#> sigma           sigma
#>                                                                                            description
#> modelName                                                                                type of model
#> contrast                                                      name of difference e.g. group1_vs_group2
#> avgAbd                                                  mean abundance value of protein in all samples
#> diff                                                                       difference among conditions
#> FDR                                                                               false discovery rate
#> statistic                                                                                 t-statistics
#> std.error                                                                               standard error
#> df                                                                                  degrees of freedom
#> p.value                                                                                        p-value
#> conf.low                                                         lower value of 95 confidence interval
#> conf.high                                                         high value of 95 confidence interval
#> sigma     residual standard deviation of linear model (needed for empirical Bayes variance shrinkage).