R/rank_group.R

In LieberInstitute/TREG: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

#' Get the Rank of the Mean expression for each Gene in each Group
#'
#' This function finds the rank of each gene's mean expression all cells in a
#' group. This data is used to compute the rank invariance value for each gene
#' later with `rank_invariance()`.
#'
#' @inheritParams rank_cells
#'
#' @return Named `list()` of ranks for each gene.
#' @export
#'
#' @examples
#' ## Rank the genes for each group defined by "group" column
#' rank_group(sce_zero_test, group_col = "group")
#' @family invariance functions
#' @importFrom purrr map
#' @importFrom rafalib splitit
#' @importFrom SummarizedExperiment assays assayNames colData
#' @importFrom Matrix rowMeans
rank_group <- function(sce, group_col = "cellType", assay = "logcounts") {
    stopifnot(group_col %in% colnames(colData(sce)))
    stopifnot(assay %in% assayNames(sce))

    ## Check for empty levels in grouping col
    if (is.factor(sce[[group_col]]) & any(table(sce[[group_col]]) == 0)) {
        warning("Dropping Empty Levels in group_col: ", group_col)
        sce[[group_col]] <- droplevels(sce[[group_col]])
    }

    group_gene_rank <- purrr::map(rafalib::splitit(sce[[group_col]]), function(indx) {
        sce_group <- sce[, indx, drop = FALSE]
        group_ranks <- rank(Matrix::rowMeans(assay(sce_group, assay)))
        return(group_ranks)
    })

    return(group_gene_rank)
}