R/plotVariableGenes.R

In navinlabcode/copykit: CopyKit

#' plotVariableGenes
#'
#' Visualization for the most variable genes found with \code{findVariableGenes}.
#'
#' @param scCNA scCNA object.
#' @param n A numeric defining how many variable genes will be plotted.
#'
#' @details \code{plotVariableGenes} plots the list of genes that was found
#' using \code{findVariableGenes}.
#'
#' @seealso \code{\link{findVariableGenes}}
#'
#' @importFrom BiocGenerics subset
#' @importFrom S4Vectors metadata subjectHits queryHits
#' @import ggplot2
#'
#' @return A ggplot object with a plot of the variable genes detected.
#' @export
#'
#' @examples
#' copykit_obj <- copykit_example_filtered()
#' copykit_obj <- findVariableGenes(copykit_obj,
#'     genes = c("FHIT", "PTEN", "FOXO1", "BRCA1")
#' )
#' plotVariableGenes(copykit_obj)
plotVariableGenes <- function(scCNA,
                              n = 30) {

    # bindings for NSE
    gene <- p1 <- NULL

    # checks
    if (is.null(S4Vectors::metadata(scCNA)$hvg)) {
        stop("Run findVariableGenes() first.")
    }

    # extracting data
    hvg_obj <- S4Vectors::metadata(scCNA)$hvg

    if (n > length(hvg_obj)) {
        n <- length(hvg_obj)
    }

    pca_df <- attr(hvg_obj, "pca_df")
    hvg_obj <- hvg_obj[1:n]
    pca_df <- pca_df[hvg_obj, ]

    pca_df <- pca_df %>%
        dplyr::mutate(gene = as.factor(gene)) %>%
        dplyr::mutate(gene = forcats::fct_reorder(gene, abs(pca_df$p1)))


    # theme setup
    my_theme <- list(
        ggplot2::theme(
            axis.title.x = element_text(colour = "gray28", size = 20),
            axis.text.x = element_text(size = 10),
            axis.ticks.x = element_blank(),
            axis.ticks.y = element_blank(),
            axis.title.y = element_text(colour = "gray28", size = 20),
            axis.text.y = element_text(size = 10),
            # axis.line.x = element_blank(),
            legend.position = "right",
            legend.title = element_blank(),
            legend.text = element_text(size = 16)
        )
    )

    p <- ggplot(pca_df, aes(x = gene, y = abs(p1))) +
        geom_segment(aes(
            x = gene,
            xend = gene,
            y = 0,
            yend = abs(p1)
        )) +
        geom_point(
            size = 4,
            fill = "#21908C",
            shape = 21
        ) +
        theme_classic() +
        coord_flip() +
        scale_y_continuous(
            expand = c(0, 0),
            limits = c(0, max(abs(pca_df$p1) + 0.02)),
            breaks = c(0, max(abs(pca_df$p1) - 0.05)),
            labels = c("Less variable", "More variable")
        ) +
        labs(
            x = "",
            y = ""
        ) +
        my_theme

    # return plot
    p
}