R/select_transcripts.R

In LieberInstitute/qsvaR: Generate Quality Surrogate Variable Analysis for Degradation Correction

#' Select transcripts associated with degradation
#'
#' Helper function to select which experimental model will be used to generate the qSVs.
#'
#' @param type A `character(1)` specifying the transcripts set type.
#' These were determined by Joshua M. Stolz et al, 2022. Here the names "cell_component", "top1500", and "standard" refer to models that were determined to be effective in removing degradation effects.
#' The "standard" model involves taking the union of the top 1000 transcripts associated with degradation from the interaction model and the main effect model.
#' The "top1500" model is the same as the "standard model except the union of the top 1500 genes associated with degradation is selected.
#' The most effective of our models, "cell_component", involved deconvolution of the degradation matrix to determine the proportion of cell types within our studied tissue.
#' These proportions were then added to our `model.matrix()` and the union of the top 1000 transcripts in the interaction model, the main effect model, and the cell proportions model were used to generate this model of qSVs.
#'
#' @return A `character()` with the transcript IDs.
#' @export
#'
#' @examples
#' ## Default set of transcripts associated with degradation
#' sig_transcripts <- select_transcripts()
#' length(sig_transcripts)
#' head(sig_transcripts)
#'
#' ## Example where match.arg() auto-completes
#' select_transcripts("top")
select_transcripts <- function(type = c("cell_component", "top1500", "standard")) {
    type <- match.arg(type)
    if (type == "cell_component") {
        return(qsvaR::transcripts$cell_component)
    } else if (type == "top1500") {
        return(qsvaR::transcripts$tx1500)
    } else if (type == "standard") {
        return(qsvaR::transcripts$standard)
    }
}