Rare_Screening: Rare_Screening: resampling-based screening with limma

In ttScreening: Genome-Wide DNA Methylation Sites Screening by Use of Training and Testing Samples

Rare_Screening: resampling-based screening with limma

Description

Rare_Screening: a resampling-based method that repeatedly samples cases and controls with replacement and applies bayes moderated linear modeling (limma) to identify predictors that are consistently significant. The function runs a rare-event resampling procedure on user provided data and returns the selected predictors along with iteration-wise selection counts.

Dr. Abrar maintains and is the author of this functions, for questions contact him at [e-mail].

Usage

Rare_Screening(predictor_list, Outcome, iteration, cut)

Arguments

predictor_list	Numeric matrix of size n x p (rows = subjects, columns = predictors). Column names are treated as predictor IDs; if missing, they will be generated.
Outcome	Integer or logical vector of length n with values in {0,1}.
iteration	Integer, number of bootstrap iterations (e.g., 100).
cut	Integer, selection count threshold (e.g., 70) used to define final selection.

Value

A list with:

• final_selection: character vector of selected predictor IDs (selection count >= cut)

• counts: integer vector of selection counts for all predictors (names = predictors)

• sel_mat: p x iteration matrix of 0/1 selections per iteration

• selected: integer indices of the predictors that met cut

Examples

## Not run: 
# predictor_list: n x p matrix; Outcome: 0/1 vector of length n
res <- Rare_Screening(predictor_list, Outcome, iteration = 100, cut = 70)
head(res$final_selection)

## End(Not run)

ttScreening documentation built on Jan. 31, 2026, 1:07 a.m.