R/plotFeaturesDetected-methods.R

In acidgenomics/minimalism: Acid Genomics Plot Functions and Themes

#' @name plotFeaturesDetected
#' @inherit AcidGenerics::plotFeaturesDetected
#' @note Updated 2023-08-11.
#'
#' @inheritParams AcidRoxygen::params
#' @param ... Additional arguments.
#'
#' @examples
#' data(
#'     RangedSummarizedExperiment,
#'     SingleCellExperiment_splatter,
#'     package = "AcidTest"
#' )
#'
#' ## SummarizedExperiment ====
#' object <- RangedSummarizedExperiment
#' plotFeaturesDetected(object)
#'
#' ## SingleCellExperiment ====
#' object <- SingleCellExperiment_splatter
#' plotFeaturesDetected(object)
NULL



## Updated 2023-08-11.
`plotFeaturesDetected,SE` <- # nolint
    function(object,
             assay = 1L,
             interestingGroups = NULL,
             limit = 0L,
             minCounts = 1L,
             labels = list(
                 "title" = "Features detected",
                 "subtitle" = NULL,
                 "x" = NULL,
                 "y" = "features"
             ),
             flip) {
        validObject(object)
        assert(
            isScalar(assay),
            isInt(limit) && isNonNegative(limit),
            isInt(minCounts) && isNonNegative(minCounts),
            isFlag(flip)
        )
        labels <- matchLabels(labels)
        interestingGroups(object) <-
            matchInterestingGroups(object, interestingGroups)
        interestingGroups <- interestingGroups(object)
        counts <- assay(object, i = assay)
        if (is(counts, "Matrix")) {
            assert(requireNamespaces("Matrix"))
            colSums <- Matrix::colSums
        }
        featureCount <- colSums(counts >= minCounts)
        data <- metrics(object)
        data[["featureCount"]] <- featureCount
        ## Plot.
        p <- ggplot(
            data = as.data.frame(data),
            mapping = aes(
                x = .data[["sampleName"]],
                y = .data[["featureCount"]],
                fill = .data[["interestingGroups"]]
            )
        ) +
            acid_geom_bar() +
            acid_scale_y_continuous_nopad()
        ## Labels.
        labels[["fill"]] <- paste(interestingGroups, collapse = ":\n")
        p <- p + do.call(what = labs, args = labels)
        ## Color palette.
        p <- p + acid_scale_fill_discrete()
        ## Show limit line.
        if (isPositive(limit)) {
            p <- p + acid_geom_abline(yintercept = limit)
        }
        ## Flip.
        if (isTRUE(flip)) {
            p <- p + acid_discrete_coord_flip()
        }
        ## Hide sample name legend.
        if (identical(interestingGroups, "sampleName")) {
            p <- p + guides(fill = "none")
        }
        ## Return.
        p
    }

formals(`plotFeaturesDetected,SE`)[["flip"]] <- # nolint
    .formalsList[["flip"]]



## Updated 2019-09-15.
`plotFeaturesDetected,SCE` <- # nolint
    function(object, ...) {
        plotFeaturesDetected(
            object = aggregateCellsToSamples(object),
            ...
        )
    }



#' @describeIn plotFeaturesDetected Applies `aggregateCellsToSamples()`
#' calculation to summarize at sample level prior to plotting.\cr
#' Passes `...` to `SummarizedExperiment` method.
#' @export
setMethod(
    f = "plotFeaturesDetected",
    signature = signature(object = "SingleCellExperiment"),
    definition = `plotFeaturesDetected,SCE`
)

#' @rdname plotFeaturesDetected
#' @export
setMethod(
    f = "plotFeaturesDetected",
    signature = signature(object = "SummarizedExperiment"),
    definition = `plotFeaturesDetected,SE`
)